Preliminary Explorations with GPT-4o(mni) Native Image Generation

Feng Zhou; Junyi Ji; Lu Yang; Mingjian Zhang; Pu Cao; Qing Song; Qingye Kong; Siqi Wu; Xuekun Zhao; Yinghui Lin

REVIEW 2 major objections 6 minor 1 cited by

Preliminary Explorations with GPT-4o(mni) Native Image Generation

T0 review · 2 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read GPT-4o native image generation handles stylization, editing, and personalization well, but falls short in spatial reasoning, temporal prediction, and knowledge-exact visuals like scientific diagrams and math plots.

desk verdict A broad, honest qualitative map of GPT-4o image generation that is useful for practitioners but overclaims in the abstract, especially on low-level processing. read the letter →

arxiv 2505.05501 v1 pith:VS3FWFD4 submitted 2025-05-06 cs.CV cs.AIeess.IV

Pu Cao , Feng Zhou , Junyi Ji , Qingye Kong , Zhixiang Lv , Mingjian Zhang , Xuekun Zhao , Siqi Wu

show 3 more authors

Yinghui Lin Qing Song Lu Yang

This is my paper · ORCID

classification cs.CVcs.AIeess.IV

keywords GPT-4oimagegenerationmultimodalqualitativeevaluationtext-to-imagespatialreasoningtemporalknowledge-basedworldmodel

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish a capability profile for GPT-4o's native image generation by testing it across six task families: traditional generation, discriminative tasks, knowledge-based generation, commonsense generation, spatially aware generation, and temporally aware generation. It argues that because the generator is embedded in a large multimodal language model, it generalizes far beyond classic text-to-image: the model performs well at stylization, editing, personalization, virtual try-on, relighting, and perceptual low-level restoration such as dehazing and old-photo repair. The paper also argues that the same architecture hits consistent walls where precision matters: it cannot control output resolution or pixel values, treats sketches, depth maps, poses, and layouts as soft suggestions, produces temporally unstable frame predictions, and hallucinates or errs in knowledge-dense domains like chemistry, biology, and mathematics. If the profile is right, it draws the boundary between a powerful creative tool and a reliable world model, and it warns that professional or safety-critical use is not yet justified.

What carries the argument

The central object under test is GPT-4o's native image generation: an image decoder built into a large multimodal language model, which lets the same model read instructions, images, and in-context examples and then draw. The argument is carried by a six-category task taxonomy adapted from earlier vision-model exploration, which organizes dozens of hand-written prompts into traditional generation, discriminative, knowledge-based, commonsense, spatially aware, and temporally aware families; the taxonomy is what turns individual samples into a capability profile. Within that structure, two structural probes do key work: resolution and aspect-ratio prompts reveal that the model can only emit three fixed sizes, and pixel-level checks (e.g., a requested five-color segmentation mask containing 22,716 unique RGB values) reveal that outputs are aligned to human perception, not numerical accuracy. Comparative probes, such as visual versus textual outputs in object detection, show that the model's discriminative performance is uneven and driven by global semantic cues.

What would settle it

Run the same six task categories with a pre-registered, independently scored prompt set and automated metrics—bounding-box IoU for layout- and pose-to-image, axis and value error for chart generation, exact-match rates for resolution and pixel-value constraints, and frame-consistency scores for temporal prediction—and check whether the qualitative profile (strong general synthesis, weak spatial, temporal, and knowledge precision) survives; the paper's own pixel analysis (22,716 unique RGB values where five were requested) already predicts that strict numerical checks will fail.

Watch

Extended reading notes

Core claim

The paper's central claim is that GPT-4o(mni) image generation is a capable general-purpose synthesizer and a limited world model at the same time. On the strength side, the authors find that the model produces vivid, semantically aligned images from ordinary and abstract prompts; renders short texts and documents; edits, inpaints, outpaints, colorizes, restores, relights, and upscales images with perceptually convincing results; preserves identity in person-driven personalization and virtual try-on; and generates coherent front, side, and back views of human subjects. On the weakness side, they find that the model only outputs three fixed image sizes, ignores explicit resolution and aspect-ratio requests, cannot produce numerically exact pixels or segmentation masks, frequently modifies content outside the region it was asked to edit or detect, fails to honor sketch, canny, depth, pose, and layout constraints with geometric fidelity, produces inconsistent multi-view geometry for rigid objects and scenes, cannot predict future or intermediate frames consistently, and makes factual and structural errors in scientific illustrations, mathematical plots, chemical structures, logos, and charts. The authors conclude that GPT-4o marks real progress in unified multimodal generation but is not yet a world model and is not yet reliable for professional or safety-critical domains.

Load-bearing premise

The load-bearing premise is that the manually curated prompts and the authors' visual inspection of the outputs represent each of the six task categories fairly, so that the reported strengths and weaknesses would survive a broader, independently scored test set; if the prompts skew easy or the examples are selected, the capability profile does not generalize.

Editorial extensions

If this is right

For creative and restoration workloads, natural-language instructions can substitute for task-specific models: style transfer, virtual try-on, relighting, colorization, dehazing, snow and rain removal, and old-photo restoration all produce usable results directly.
Any application requiring exact geometry or measurement inherits the model's limits: output size is locked to three resolutions, requested aspect ratios are rounded, and pixel-level numerical constraints are not honored.
Because the model modifies content beyond masked or requested regions during inpainting, editing, and detection, it cannot serve as a drop-in tool where input integrity is contractual, such as industrial inspection or forensic image work.
Temporal prediction across frames is not consistent enough for video generation or for treating the model as a physical world simulator, even though single-frame physics commonsense is often plausible.
Knowledge-dense visualizations such as charts, scientific diagrams, molecular structures, math plots, and logos are generated as plausible-looking images rather than accurate ones, so they need verification before any informative use.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A plausible reading of the evidence is that this generation architecture is strong under soft semantic control (style, mood, identity, global composition) and weak under hard constraints (coordinates, counts, time steps, exact values); a direct testable extension is to measure whether giving the model a code-generated draft, such as a plotted chart or rendered layout to copy, closes the precision
The consistent pattern of perceptual plausibility over numerical exactness suggests the model could reliably serve as a data-augmentation engine for low-level vision training sets, where visual realism matters more than calibrated ground truth.
The contrast between strong human-centric view synthesis and weak rigid-object or scene geometry hints that the apparent 3D ability may ride on large portrait and identity priors rather than volumetric reasoning; testing with novel, unseen object categories would isolate which.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a useful, honest, but methodologically thin qualitative sweep of GPT-4o image generation. The central capability profile—strong at general synthesis and personalization, weak at spatial/temporal precision and knowledge-dense content—is plausible and matches community experience, but the evidence is curated examples, and the abstract's "strong low-level image processing" goes beyond what the paper's own low-level section shows.

What's new is the breadth: applying a broad task taxonomy to this specific model version, with a large bank of examples covering text-to-image, editing, personalization, spatial control, discriminative tasks, physics, chemistry, biology, math, commonsense, multi-view, and temporal prediction. The paper is transparent about its preliminary and qualitative nature, and the limitations section is reasonable. Several findings—strong multi-concept personalization, spatial inputs treated as soft guidance, weak temporal consistency—are useful to practitioners, and the paper does not hide specific failures like reflection removal deleting non-reflective content.

The soft spots are the ones you'd expect. There is no sampling protocol for prompts, no error rates, no scoring rubric, no inter-annotator check, no comparison baseline, and no public release of prompts or outputs. That makes the reported strengths and weaknesses hard to verify or generalize. The stress-test concern is on target: the abstract's phrase "strong capabilities in ... low-level image processing" is contradicted by the body's own descriptions. Denoising "often alters certain local structures," deblurring has "color deviations," deraining "over-smooths" and hallucinates, reflection removal removes non-reflective objects, and underwater enhancement "varies." If the authors want to keep the abstract, they need evidence or a weighting; otherwise they should soften it. This matters because the abstract's capability profile is the part people will cite.

The paper also has signs of being rushed: a duplicated sentence in the introduction, an [A] placeholder, "Fig. X" references, and typos. These are minor relative to the methodological issue but reinforce its tech-report character.

Who it's for: practitioners deciding whether to trust GPT-4o outputs in a given task, and researchers wanting a quick map of known failure modes. It is not a benchmark and should not be used as one.

Recommendation: I would not desk-reject it, but I also would not accept it as is. It deserves serious peer review with the expectation of major revision: release the prompt set and outputs, replace or quantify the "strong" low-level claim, and cut or clearly mark the speculative parts. With those changes it could be a solid qualitative contribution.

Referee Report

2 major / 6 minor

Summary. The paper presents a preliminary, purely qualitative evaluation of OpenAI's GPT-4o native image generation mode across six task families: traditional image generation (text-to-image, multimodal-conditioned, low-level processing), discriminative generation (detection, segmentation, counting, human-centric tasks, depth/normal/flow estimation, change detection), knowledge-based generation (physics, chemistry, biology, mathematics, agriculture), commonsense-based generation, spatially-aware generation (multi-view, novel-view, spatial reasoning), and temporally-aware generation. The authors report strengths in text-to-image synthesis, stylization, personalization, and certain low-level processing tasks, while identifying limitations in precise spatial control, instruction grounding, temporal consistency, and knowledge-intensive generation. The evidence consists of manually curated prompts and the authors' visual inspection of the generated images; no quantitative metrics, no comparison baselines, and no released prompt/output sets are provided.

Significance. If the reported capability profile is accurate, this is a useful early map of a rapidly evolving model family: the six-task taxonomy is broad, the figure set is extensive, and the prompts are embedded in the figures, which permits partial reproducibility. The paper also deserves credit for explicitly self-identifying as qualitative and for including a limitations section that names four concrete failure modes. However, the central claims are not supported with the rigor that would let a reader distinguish a genuine capability profile from a favorable reading of hand-picked examples: there is no scoring rubric, no inter-annotator agreement, no error rates, and no baseline comparison. Most importantly, the abstract's assertion of 'strong capabilities in ... low-level image processing' is in tension with the paper's own detailed findings in Section 3.3, which repeatedly document content alteration, structural distortion, and hallucination. Because the capability profile is the paper's main contribution, this overclaim and the methodological gaps are load-bearing.

major comments (2)

[Abstract / §3.3] The abstract's claim that GPT-4o 'performs impressively well in ... low-level image processing' is not supported by the paper's own detailed findings in Section 3.3: image denoising 'often alters certain local structures' with 'degradation in structural fidelity' (Fig. 44); image deblurring produces 'appearance inconsistencies' and 'color deviations' (Fig. 45); image deraining 'over-smooths textures' and produces 'content hallucination' (Figs. 48-49); reflection removal deletes non-reflective content including eyeglasses and reconstructs buildings inconsistently (Fig. 55); shadow removal changes pebble shapes (Fig. 54); and underwater enhancement is reported only as 'certain' and 'varies' (Fig. 58). Because the paper provides no scoring rubric, no quantitative fidelity metric, and no inter-annotator check, the reader cannot distinguish a genuine strength from a favorable reading of mixed examples; the authors should either soften the abstract's 'strong' wording for low-level processing or systematically balance it against the documented failure modes with a transparent aggregation protocol.
[§1] All qualitative conclusions about the capability profile rest on the assumption stated in Section 1 that 'we have manually curated a representative set of instruction prompts' and on the authors' subjective visual inspection, but the paper provides no protocol for prompt selection, no release of the full prompt set or generated outputs, no inter-annotator agreement, no error rates, and no comparison baseline. Since Sections 3 through 8 draw general capability conclusions from a handful of hand-picked examples per task, the representativeness and unbiasedness of this evidence is load-bearing; the authors should publish the complete prompt set and outputs (or a substantial random sample), define a transparent scoring rubric (e.g., pass/fail per example with a failure taxonomy), and have at least one additional annotator independently score a random subset so that the reported strengths and weaknesses can be verified.

minor comments (6)

[§3.2.2] In the paragraph on interaction-driven generation, the text refers to 'as illustrated in Fig. X'; no Figure X exists, and the intended reference is likely Fig. 25, which should be corrected.
[§3.3.8] The opening sentence contains an empty citation: 'applying specific visual artifacts or environmental conditions to clean images[]'; the missing reference should be supplied.
[§1] The paragraph on limitations contains a duplicated sentence: 'Based on the aforementioned analyses and experimental results, we further discuss the current limitations encountered by the image generation model in Sec. 9.' appears twice verbatim; one instance should be removed.
[Various figure captions and section headers] Several typographical errors appear in headers and captions, including 'Generaiton' (§3.2), 'Inpainitng & Outpainting' (§3.2.4), 'Shasow Removal' (§3.3.4), 'Reflction Removal' (§3.3.5), 'Spatical Reasoning' (§7.3), 'perosn-driven' (§3.2.2), 'Vitural try-on' (§3.2.6), 'Exampes' (Fig. 42), and 'photoreadlistic' (Figs. 117 and 119); these should be corrected.
[§3.3.8] The note under Figs. 59-60 ('some prompts include degradation details to enhance output quality in practice') is vague; please specify which prompts received additional degradation details and why, so readers can interpret the results correctly.
[Reference [194]] The paper cites 'previous study[194]' for the GPT-4V exploration that inspired the task taxonomy, and also refers to '[A]' when describing the personalization evaluation protocols in §3.2.2; both references should be fully expanded in the bibliography and properly numbered, since they are load-bearing for the paper's methodological lineage.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a qualitative black-box evaluation whose conclusions are not derived from its inputs.

full rationale

This paper is an empirical, qualitative exploration of a proprietary black-box model, GPT-4o. Section 1 explicitly frames the report as 'a qualitative exploration of the diverse capabilities exhibited by GPT-4o, rather than providing rigorous quantitative performance metrics or focusing comparisons with other models.' The conclusions are drawn from manually curated prompts and visual inspection of generated outputs; there is no derivation chain, no fitted parameter, no equation that predicts an output from an input, and no uniqueness theorem invoked to force a choice. The only references that could carry argumentative weight are [194], cited to inspire the task taxonomy, and the unresolved '[A]' in Section 3.2.2 for personalization evaluation protocols; neither makes the empirical findings equivalent to their inputs, and the taxonomy does not predetermine which capabilities are reported as strong or weak. The paper's own detailed results provide independent evidence for its qualified conclusions, e.g., Section 3.3.3 notes that denoising 'often alters certain local structures' and deblurring shows 'color deviations,' while Section 7.2.1 reports 'inaccurate alignment of roofs, inconsistent vanishing points.' The abstract's claim of 'strong capabilities in ... low-level image processing' is arguably in tension with some of these caveats, but that is a calibration or correctness concern, not circularity. No circular step can be quoted and exhibited, so the score is 0. The unresolved citation '[A]' is a missing-reference issue rather than a circularity issue.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters and no invented entities. It depends on the domain assumptions listed above: that the API is representative, that visual inspection is a valid metric, and that the curated prompts cover each task. Since this is an empirical exploration rather than a theoretical derivation, the axiom ledger reflects evaluation assumptions rather than mathematical axioms.

assumptions (3)

domain assumption The OpenAI API endpoint used in May 2025 accurately reflects the deployed GPT-4o(mni) model and behaves consistently during the test window.
The paper quotes specific model outputs but does not pin down a model version, API parameters, or retraining dates, so reproducibility depends on the assumption that the API behavior is stable.
domain assumption Visual inspection of the generated images by the authors is a reliable and unbiased measure of task success.
No quantitative metrics, inter-annotator agreement, or structured rubrics are provided; all conclusions rely on the authors' subjective reading of the examples.
domain assumption The manually curated prompt set adequately covers the range of each task category.
The paper states prompts were 'carefully designed or automatically generated with the assistance of ChatGPT' (Section 1), but no protocol or sampling strategy is documented, so coverage is not externally validated.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Preliminary Explorations with GPT-4o(mni) Native Image Generation." pith.science (2026). https://pith.science/paper/VS3FWFD4

@misc{pith2026250505501,
  author       = {Pith},
  title        = {Pith review of: Preliminary Explorations with GPT-4o(mni) Native Image Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VS3FWFD4}},
  note         = {Machine review of arXiv:2505.05501}
}

read the original abstract

Recently, the visual generation ability by GPT-4o(mni) has been unlocked by OpenAI. It demonstrates a very remarkable generation capability with excellent multimodal condition understanding and varied task instructions. In this paper, we aim to explore the capabilities of GPT-4o across various tasks. Inspired by previous study, we constructed a task taxonomy along with a carefully curated set of test samples to conduct a comprehensive qualitative test. Benefiting from GPT-4o's powerful multimodal comprehension, its image-generation process demonstrates abilities surpassing those of traditional image-generation tasks. Thus, regarding the dimensions of model capabilities, we evaluate its performance across six task categories: traditional image generation tasks, discriminative tasks, knowledge-based generation, commonsense-based generation, spatially-aware image generation, and temporally-aware image generation. These tasks not only assess the quality and conditional alignment of the model's outputs but also probe deeper into GPT-4o's understanding of real-world concepts. Our results reveal that GPT-4o performs impressively well in general-purpose synthesis tasks, showing strong capabilities in text-to-image generation, visual stylization, and low-level image processing. However, significant limitations remain in its ability to perform precise spatial reasoning, instruction-grounded generation, and consistent temporal prediction. Furthermore, when faced with knowledge-intensive or domain-specific scenarios, such as scientific illustrations or mathematical plots, the model often exhibits hallucinations, factual errors, or structural inconsistencies. These findings suggest that while GPT-4o marks a substantial advancement in unified multimodal generation, there is still a long way to go before it can be reliably applied to professional or safety-critical domains.

Figures

Figures reproduced from arXiv: 2505.05501 by the authors.

**Figure 2.** GPT-4o is unable to strictly follow aspect ratio prompts and instead selects from a limited [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 4.** Examples of text-to-image generation results by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗

Figures from the paper (138 more)

**Figure 5.** Figure 5: Examples of abstract text-to-image generation results by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

**Figure 6.** Figure 6: Examples of complex text-to-image generation results by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

**Figure 7.** Figure 7: Examples of text rendering in stylized scenes. GPT-4o embeds short texts into posters, [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]

**Figure 8.** Figure 8: Examples of long text rendering tasks, including book cover paragraphs and movie synopses. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]

**Figure 9.** Figure 9: Multilingual text rendering across nine languages. Non-Latin scripts often show rendering [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]

**Figure 10.** Figure 10: Examples of textual document image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]

**Figure 11.** Figure 11: Examples of catalog image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]

**Figure 12.** Figure 12: Examples of markdown-to-table generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p025_12.png]

**Figure 13.** Figure 13: Examples of chart generation by GPT-4o. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_13.png]

**Figure 14.** Figure 14: Additional examples of chart generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p027_14.png]

**Figure 15.** Figure 15: Examples of concept replacement, where GPT-4o replaces target objects or backgrounds [PITH_FULL_IMAGE:figures/full_fig_p029_15.png]

**Figure 16.** Figure 16: Examples of concept removal tasks, where GPT-4o eliminates specific objects (e.g., [PITH_FULL_IMAGE:figures/full_fig_p030_16.png]

**Figure 17.** Figure 17: Complex image editing examples involving multiple changes, such as swapping accessories [PITH_FULL_IMAGE:figures/full_fig_p031_17.png]

**Figure 18.** Figure 18: Additional examples of multi-step and stylistic edits, where GPT-4o is tasked with [PITH_FULL_IMAGE:figures/full_fig_p032_18.png]

**Figure 19.** Figure 19: Examples of subject-driven image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p035_19.png]

**Figure 20.** Figure 20: Examples of style-driven image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p036_20.png]

**Figure 21.** Figure 21: Examples of person-driven image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p037_21.png]

**Figure 22.** Figure 22: Additional examples of perosn-driven image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p038_22.png]

**Figure 23.** Figure 23: Examples of scene-driven image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p039_23.png]

**Figure 24.** Figure 24: Examples of pose-driven image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p040_24.png]

**Figure 25.** Figure 25: Examples of interaction-driven image generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p041_25.png]

**Figure 26.** Figure 26: Examples of multi-concept personalization results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p042_26.png]

**Figure 27.** Figure 27: Additional examples of multi-concept personalization results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p043_27.png]

**Figure 28.** Figure 28: Examples of fine-grained decoupled personalization results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p044_28.png]

**Figure 29.** Figure 29: Examples of layout-to-image results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p047_29.png]

**Figure 30.** Figure 30: Examples of sketch-to-image results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p048_30.png]

**Figure 31.** Figure 31: Examples of canny-to-image results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p049_31.png]

**Figure 32.** Figure 32: Examples of depth-to-image results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p050_32.png]

**Figure 33.** Figure 33: Examples of pose-to-image results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p051_33.png]

**Figure 34.** Figure 34: Examples of multi-conditioned spatial control results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p052_34.png]

**Figure 35.** Figure 35: Examples of image inpainting generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p054_35.png]

**Figure 36.** Figure 36: Examples of image outpainting generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p055_36.png]

**Figure 37.** Figure 37: Examples of story visualization generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p057_37.png]

**Figure 38.** Figure 38: Additional examples of story visualization generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p058_38.png]

**Figure 39.** Figure 39: Examples of virtual try-on generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p060_39.png]

**Figure 40.** Figure 40: Examples of image super-resolution results generated by GPT-4o. The model can enhance [PITH_FULL_IMAGE:figures/full_fig_p062_40.png]

**Figure 41.** Figure 41: Additional examples of image super-resolution results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p063_41.png]

**Figure 42.** Figure 42: Exampes of image colorization generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p065_42.png]

**Figure 43.** Figure 43: Additional examples of image colorization generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p066_43.png]

**Figure 44.** Figure 44: Examples of GPT-4o performing image denoising. While the model is able to reduce noise [PITH_FULL_IMAGE:figures/full_fig_p069_44.png]

**Figure 45.** Figure 45: Examples of image deblurring results generated by GPT-4o. The model is able to restore [PITH_FULL_IMAGE:figures/full_fig_p070_45.png]

**Figure 46.** Figure 46: Examples of image dehazing results generated by GPT-4o. The model effectively removes [PITH_FULL_IMAGE:figures/full_fig_p071_46.png]

**Figure 47.** Figure 47: Additional examples of image dehazing results generated by GPT-4o. The model not [PITH_FULL_IMAGE:figures/full_fig_p072_47.png]

**Figure 48.** Figure 48: Examples of image deraining results generated by GPT-4o. The model can effectively [PITH_FULL_IMAGE:figures/full_fig_p073_48.png]

**Figure 49.** Figure 49: Examples of raindrop removal results generated by GPT-4o. The model effectively [PITH_FULL_IMAGE:figures/full_fig_p074_49.png]

**Figure 50.** Figure 50: Examples of image desnowing results generated by GPT-4o. The model effectively [PITH_FULL_IMAGE:figures/full_fig_p075_50.png]

**Figure 51.** Figure 51: Examples of low-light image enhancement results generated by GPT-4o. The model [PITH_FULL_IMAGE:figures/full_fig_p076_51.png]

**Figure 52.** Figure 52: Examples of old photo restoration results generated by GPT-4o. The model effectively [PITH_FULL_IMAGE:figures/full_fig_p077_52.png]

**Figure 53.** Figure 53: Additional examples of old photo restoration results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p078_53.png]

**Figure 54.** Figure 54: Examples of shadow removal results generated by GPT-4o. The model effectively removes [PITH_FULL_IMAGE:figures/full_fig_p080_54.png]

**Figure 55.** Figure 55: Examples of reflection removal results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p082_55.png]

**Figure 56.** Figure 56: Examples of image relighting with textual prompts generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p084_56.png]

**Figure 57.** Figure 57: Examples of image relighting with reference images generated by GPT-4o. 85 [PITH_FULL_IMAGE:figures/full_fig_p085_57.png]

**Figure 58.** Figure 58: Examples of underwater image enhancement results generated by GPT-4o. The model [PITH_FULL_IMAGE:figures/full_fig_p087_58.png]

**Figure 59.** Figure 59: Examples of low-level data synthesis results generated by GPT-4o for a skateboard scene. [PITH_FULL_IMAGE:figures/full_fig_p089_59.png]

**Figure 60.** Figure 60: Examples of low-level data synthesis results generated by GPT-4o for a forest scene. The [PITH_FULL_IMAGE:figures/full_fig_p090_60.png]

**Figure 61.** Figure 61: Examples of general object detection results generated by GPT-4o for detecting dogs. The [PITH_FULL_IMAGE:figures/full_fig_p092_61.png]

**Figure 62.** Figure 62: Examples of general object detection results generated by GPT-4o for detecting cats. In [PITH_FULL_IMAGE:figures/full_fig_p093_62.png]

**Figure 63.** Figure 63: Examples of vehicle detection results in satellite imagery generated by GPT-4o. The [PITH_FULL_IMAGE:figures/full_fig_p095_63.png]

**Figure 64.** Figure 64: Additional examples of vehicle detection results in satellite imagery generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p096_64.png]

**Figure 65.** Figure 65: Examples of surface defect detection results generated by GPT-4o on NEU-DET [PITH_FULL_IMAGE:figures/full_fig_p098_65.png]

**Figure 66.** Figure 66: Examples of PCB defect detection results generated by GPT-4o on DeepDCB dataset[ [PITH_FULL_IMAGE:figures/full_fig_p099_66.png]

**Figure 67.** Figure 67: Examples of semantic segmentation results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p101_67.png]

**Figure 68.** Figure 68: Examples of instance segmentation results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p102_68.png]

**Figure 69.** Figure 69: Examples of panoptic segmentation results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p103_69.png]

**Figure 70.** Figure 70: Examples of instance segmentation results with task instruction generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p104_70.png]

**Figure 71.** Figure 71: Examples of semantic segmentation with in-context learning generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p105_71.png]

**Figure 72.** Figure 72: Examples of semantic segmentation with in-context learning generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p106_72.png]

**Figure 73.** Figure 73: Examples of object counting results by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p108_73.png]

**Figure 74.** Figure 74: Examples of human pose estimation results by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p110_74.png]

**Figure 75.** Figure 75: Additional Examples of human pose estimation results by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p111_75.png]

**Figure 76.** Figure 76: Additional examples of human pose estimation results by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p112_76.png]

**Figure 77.** Figure 77: Examples of human parsing generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p114_77.png]

**Figure 78.** Figure 78: Additional examples of human parsing generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p115_78.png]

**Figure 79.** Figure 79: Examples of human mesh recovery generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p117_79.png]

**Figure 88.** Figure 88: Examples of force effect results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p131_88.png]

**Figure 89.** Figure 89: Examples of force effect results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p132_89.png]

**Figure 90.** Figure 90: Examples of optical results generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p133_90.png]

**Figure 91.** Figure 91: Examples of optical results generation by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p134_91.png]

**Figure 92.** Figure 92: Examples of optical results generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p135_92.png]

**Figure 93.** Figure 93: Examples of thermodynamic effect generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p136_93.png]

**Figure 94.** Figure 94: Examples of thermodynamic effect generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p137_94.png]

**Figure 95.** Figure 95: Examples of material properties generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p138_95.png]

**Figure 96.** Figure 96: Examples of material properties generated by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p139_96.png]

**Figure 97.** Figure 97: Examples of chemistry-related generation results, including molecule models, reaction [PITH_FULL_IMAGE:figures/full_fig_p141_97.png]

**Figure 98.** Figure 98: Examples of laboratory scene generation for chemistry experiments, with several incorrect [PITH_FULL_IMAGE:figures/full_fig_p142_98.png]

**Figure 99.** Figure 99: Examples of chemical structure generation results, with some structural errors and [PITH_FULL_IMAGE:figures/full_fig_p143_99.png]

**Figure 100.** Figure 100: Examples of biology-related image generation, including bacterial colonies, genetic [PITH_FULL_IMAGE:figures/full_fig_p145_100.png]

**Figure 101.** Figure 101: Examples of biology image analysis tasks, including cell segmentation and immunofluo [PITH_FULL_IMAGE:figures/full_fig_p146_101.png]

**Figure 102.** Figure 102: Examples of function plotting generation, with common mistakes in axis labeling and [PITH_FULL_IMAGE:figures/full_fig_p148_102.png]

**Figure 103.** Figure 103: Examples of geometry diagram generation, with incorrect geometric relationships and [PITH_FULL_IMAGE:figures/full_fig_p149_103.png]

**Figure 104.** Figure 104: Examples of agriculture knowledge-based generation, including pest identification, crop [PITH_FULL_IMAGE:figures/full_fig_p151_104.png]

**Figure 105.** Figure 105: Examples of synthetic agricultural image generation, covering fruit crops, farmland [PITH_FULL_IMAGE:figures/full_fig_p152_105.png]

**Figure 106.** Figure 106: Examples of landmark image generation, showcasing different iconic cultural and [PITH_FULL_IMAGE:figures/full_fig_p154_106.png]

**Figure 107.** Figure 107: Examples of festival image generation, showing traditional customs and cultural celebra [PITH_FULL_IMAGE:figures/full_fig_p155_107.png]

**Figure 108.** Figure 108: Examples of food generation conditioned on different cultural recipes and cooking styles. [PITH_FULL_IMAGE:figures/full_fig_p156_108.png]

**Figure 109.** Figure 109: Examples of clothing generation guided by regional fashion styles and garment design. [PITH_FULL_IMAGE:figures/full_fig_p157_109.png]

**Figure 110.** Figure 110: More examples of clothing image generation with style control and appearance variation. [PITH_FULL_IMAGE:figures/full_fig_p158_110.png]

**Figure 111.** Figure 111: Examples of painting generation with various art styles and cultural elements. [PITH_FULL_IMAGE:figures/full_fig_p159_111.png]

**Figure 112.** Figure 112: Examples of building generation based on architectural styles and cultural background. [PITH_FULL_IMAGE:figures/full_fig_p160_112.png]

**Figure 113.** Figure 113: Examples of literature-related generation, including book covers and literary visual [PITH_FULL_IMAGE:figures/full_fig_p161_113.png]

**Figure 114.** Figure 114: Examples of logo generation. GPT-4o tends to generate factual errors, such as wrong [PITH_FULL_IMAGE:figures/full_fig_p162_114.png]

**Figure 115.** Figure 115: Examples of health and safety-related generation. [PITH_FULL_IMAGE:figures/full_fig_p163_115.png]

**Figure 116.** Figure 116: Examples of multi-view image generation results produced by GPT-4o. The model is [PITH_FULL_IMAGE:figures/full_fig_p165_116.png]

**Figure 117.** Figure 117: Examples of angle-specific multi-view image generation results by GPT-4o. The [PITH_FULL_IMAGE:figures/full_fig_p166_117.png]

**Figure 118.** Figure 118: Examples of multi-view human image generation results by GPT-4o. The model [PITH_FULL_IMAGE:figures/full_fig_p167_118.png]

**Figure 119.** Figure 119: Examples of photorealistic multi-view human image generation by GPT-4o. The model [PITH_FULL_IMAGE:figures/full_fig_p168_119.png]

**Figure 120.** Figure 120: Examples of multi-view scene generation results by GPT-4o. The model attempts to [PITH_FULL_IMAGE:figures/full_fig_p169_120.png]

**Figure 121.** Figure 121: Examples of novel-view synthesis on normal targets by GPT-4o. The model generates [PITH_FULL_IMAGE:figures/full_fig_p171_121.png]

**Figure 122.** Figure 122: Examples of novel-view synthesis on cartoon-style normal targets by GPT-4o. The [PITH_FULL_IMAGE:figures/full_fig_p172_122.png]

**Figure 123.** Figure 123: Examples of novel-view synthesis on structurally simple normal targets by GPT-4o. [PITH_FULL_IMAGE:figures/full_fig_p173_123.png]

**Figure 124.** Figure 124: Examples of novel-view synthesis on face portrait (ID-style) images by GPT-4o. The [PITH_FULL_IMAGE:figures/full_fig_p174_124.png]

**Figure 125.** Figure 125: Additional examples of novel-view synthesis on face portrait (ID-style) images by [PITH_FULL_IMAGE:figures/full_fig_p175_125.png]

**Figure 126.** Figure 126: Examples of novel-view synthesis on full-body human images by GPT-4o. The model is [PITH_FULL_IMAGE:figures/full_fig_p176_126.png]

**Figure 127.** Figure 127: Examples of novel-view synthesis on complex outdoor scenes by GPT-4o. The model [PITH_FULL_IMAGE:figures/full_fig_p177_127.png]

**Figure 128.** Figure 128: More examples of novel-view synthesis on complex scenes by GPT-4o. The model [PITH_FULL_IMAGE:figures/full_fig_p178_128.png]

**Figure 129.** Figure 129: Examples of novel-view synthesis on indoor (embodied) scenes by GPT-4o. The model [PITH_FULL_IMAGE:figures/full_fig_p180_129.png]

**Figure 130.** Figure 130: Examples of novel-view synthesis based on layout images by GPT-4o. The model can [PITH_FULL_IMAGE:figures/full_fig_p181_130.png]

**Figure 131.** Figure 131: Examples of spatial reasoning through textual prompts by GPT-4o. The model demon [PITH_FULL_IMAGE:figures/full_fig_p183_131.png]

**Figure 132.** Figure 132: Examples of image-based spatial reasoning by GPT-4o. The model successfully integrates [PITH_FULL_IMAGE:figures/full_fig_p184_132.png]

**Figure 133.** Figure 133: More examples of image-based spatial reasoning by GPT-4o. The model is able to [PITH_FULL_IMAGE:figures/full_fig_p185_133.png]

**Figure 134.** Figure 134: Examples of spatial reasoning through 2D-to-3D reassembly by GPT-4o. Given flat [PITH_FULL_IMAGE:figures/full_fig_p186_134.png]

**Figure 135.** Figure 135: Examples of spatial reasoning through localized object editing by GPT-4o. The model [PITH_FULL_IMAGE:figures/full_fig_p187_135.png]

**Figure 136.** Figure 136: Example of future frame prediction by GPT-4o. Given three input frames showing a [PITH_FULL_IMAGE:figures/full_fig_p189_136.png]

**Figure 137.** Figure 137: Example of future frame prediction by GPT-4o. Although the model produces a visually [PITH_FULL_IMAGE:figures/full_fig_p190_137.png]

**Figure 138.** Figure 138: Example of future frame prediction by GPT-4o. While the generated image maintains [PITH_FULL_IMAGE:figures/full_fig_p191_138.png]

**Figure 139.** Figure 139: Example of future frame prediction by GPT-4o. Although the overall scene and context [PITH_FULL_IMAGE:figures/full_fig_p192_139.png]

**Figure 140.** Figure 140: Example of future frame prediction by GPT-4o. The generated frame maintains strong [PITH_FULL_IMAGE:figures/full_fig_p193_140.png]

**Figure 141.** Figure 141: Example of intermediate frame prediction by GPT-4o. Given the first and third frames, [PITH_FULL_IMAGE:figures/full_fig_p194_141.png]

**Figure 142.** Figure 142: Example of intermediate frame prediction by GPT-4o. The generated frame fails to [PITH_FULL_IMAGE:figures/full_fig_p195_142.png]

**Figure 143.** Figure 143: Example of intermediate frame prediction by GPT-4o. The generated frame demonstrates [PITH_FULL_IMAGE:figures/full_fig_p196_143.png]

**Figure 144.** Figure 144: Example of intermediate frame prediction by GPT-4o. The generated frame fails to [PITH_FULL_IMAGE:figures/full_fig_p197_144.png]

**Figure 145.** Figure 145: Example of intermediate frame prediction by GPT-4o. The generated frame exhibits [PITH_FULL_IMAGE:figures/full_fig_p198_145.png]

**Figure 146.** Figure 146: Example of past frame prediction by GPT-4o. Although the model attempts to infer a [PITH_FULL_IMAGE:figures/full_fig_p199_146.png]

**Figure 147.** Figure 147: Example of past frame prediction by GPT-4o. The generated image appears to prioritize [PITH_FULL_IMAGE:figures/full_fig_p200_147.png]

**Figure 148.** Figure 148: Example of past frame prediction by GPT-4o. Although the generated image visually [PITH_FULL_IMAGE:figures/full_fig_p201_148.png]

**Figure 149.** Figure 149: Example of past frame prediction by GPT-4o. The model demonstrates a reasonable [PITH_FULL_IMAGE:figures/full_fig_p202_149.png]

**Figure 150.** Figure 150: Example of past frame prediction by GPT-4o. The model successfully captures the [PITH_FULL_IMAGE:figures/full_fig_p203_150.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

PosterHarness: Turning Scientific Poster Generation into an Auditable Instruction-Following Benchmark
cs.CV 2026-07 conditional novelty 6.5 of 10

A placeholder-first harness separates visual poster design from scientific figure grounding, turning poster generation into measurable instruction-following with a 12-paper pilot and failure taxonomy.

Reference graph

Works this paper leans on

219 extracted references · 16 canonical work pages · cited by 1 Pith paper

[1]

Defocus deblurring using dual-pixel data

Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16, pages 111–126. Springer, 2020

2020
[2]

Learning to reduce defocus blur by realistically modeling dual-pixel data

Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S Brown, and Peyman Milanfar. Learning to reduce defocus blur by realistically modeling dual-pixel data. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 2289– 2298, 2021

2021
[3]

Ntire 2017 challenge on single image super-resolution: Dataset and study

Eirikur Agustsson and Radu Timofte. Ntire 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 126–135, 2017

2017
[4]

Dream360: Diverse and immersive outdoor virtual scene creation via transformer-based 360 image outpainting

Hao Ai, Zidong Cao, Haonan Lu, Chen Chen, Jian Ma, Pengyuan Zhou, Tae-Kyun Kim, Pan Hui, and Lin Wang. Dream360: Diverse and immersive outdoor virtual scene creation via transformer-based 360 image outpainting. IEEE transactions on visualization and computer graphics, 2024

2024
[5]

Single-image reflection removal using deep learning: a systematic review

Ali Amanlou, Amir Abolfazl Suratgar, Jafar Tavoosi, Ardashir Mohammadzadeh, and Amir Mosavi. Single-image reflection removal using deep learning: a systematic review. IEEE Access, 10:29937–29953, 2022

2022
[6]

2d human pose esti- mation: New benchmark and state of the art analysis

Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, and Bernt Schiele. 2d human pose esti- mation: New benchmark and state of the art analysis. In Proceedings of the IEEE Conference on computer Vision and Pattern Recognition, pages 3686–3693, 2014

2014
[7]

Change detection techniques for remote sensing applications: A survey

Anju Asokan and JJESI Anitha. Change detection techniques for remote sensing applications: A survey. Earth Science Informatics, 12:143–160, 2019

2019
[8]

Rethinking inductive biases for surface normal estimation

Gwangbin Bae and Andrew J Davison. Rethinking inductive biases for surface normal estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9535–9545, 2024

2024

Show all 219 references

[9]

ediffi: Text-to-image diffusion models with an ensemble of expert denoisers

Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, et al. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022

2022 arXiv
[10]

Diffusion models through a global lens: Are they culturally inclusive? arXiv preprint arXiv:2502.08914, 2025

Zahra Bayramli, Ayhan Suleymanzade, Na Min An, Huzama Ahmad, Eunsu Kim, Junyeong Park, James Thorne, and Alice Oh. Diffusion models through a global lens: Are they culturally inclusive? arXiv preprint arXiv:2502.08914, 2025

2025 arXiv
[11]

Adabins: Depth estimation using adaptive bins

Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka. Adabins: Depth estimation using adaptive bins. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4009–4018, 2021

2021
[12]

Ledits++: Limitless image editing using text-to- image models

Manuel Brack, Felix Friedrich, Katharia Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, and Apolinário Passos. Ledits++: Limitless image editing using text-to- image models. pages 8861–8870, 2024

2024
[13]

Instructpix2pix: Learning to follow image editing instructions

Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18392–18402, 2023

2023
[14]

Learning to generate realistic noisy images via pixel-level noise-aware adversarial training

Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, and Donglai Wei. Learning to generate realistic noisy images via pixel-level noise-aware adversarial training. Advances in Neural Information Processing Systems, 34:3259–3270, 2021

2021
[15]

Decoupled textual embeddings for customized image generation

Yufei Cai, Yuxiang Wei, Zhilong Ji, Jinfeng Bai, Hu Han, and Wangmeng Zuo. Decoupled textual embeddings for customized image generation. arXiv preprint arXiv:2312.11826, 2023

2023 arXiv
[16]

Controllable generation with text-to-image diffusion models: A survey

Pu Cao, Feng Zhou, Qing Song, and Lu Yang. Controllable generation with text-to-image diffusion models: A survey. arXiv preprint arXiv:2403.04279, 2024. 207

2024
[17]

End-to-end object detection with transformers

Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213–229. Springer, 2020

2020
[18]

Generative novel view synthesis with 3d-aware diffusion models

Eric R Chan, Koki Nagano, Matthew A Chan, Alexander W Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. Generative novel view synthesis with 3d-aware diffusion models. In Proceedings of the IEEE/CVF International Conferenc...

2023
[19]

Spatialvlm: Endowing vision-language models with spatial reasoning capabilities

Boyuan Chen, Zhuo Xu, Sean Kirmani, Brain Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. Spatialvlm: Endowing vision-language models with spatial reasoning capabilities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14455–14465, 2024

2024
[20]

Artadapter: Text-to-image style transfer using multi-level style encoder and explicit adaptation

Dar-Yen Chen, Hamish Tennent, and Ching-Wen Hsu. Artadapter: Text-to-image style transfer using multi-level style encoder and explicit adaptation. arXiv preprint arXiv:2312.02109, 2023

2023 arXiv
[21]

Disen- booth: Disentangled parameter-efficient tuning for subject-driven text-to-image generation

Hong Chen, Yipeng Zhang, Xin Wang, Xuguang Duan, Yuwei Zhou, and Wenwu Zhu. Disen- booth: Disentangled parameter-efficient tuning for subject-driven text-to-image generation. arXiv preprint arXiv:2305.03374, 2023

2023 arXiv
[22]

Textdiffuser-2: Unleashing the power of language models for text rendering

Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, and Furu Wei. Textdiffuser-2: Unleashing the power of language models for text rendering. arXiv preprint arXiv:2311.16465, 2023

2023 arXiv
[23]

Textdiffuser: Diffusion models as text painters

Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, and Furu Wei. Textdiffuser: Diffusion models as text painters. arXiv preprint arXiv:2305.10855, 2023

2023 arXiv
[24]

Semantic image segmentation with deep convolutional nets and fully connected crfs

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062, 2014

2014 arXiv
[25]

An empirical study of gpt-4o image generation capabilities

Sixiang Chen, Jinbin Bai, Zhuoran Zhao, Tian Ye, Qingyu Shi, Donghao Zhou, Wenhao Chai, Xin Lin, Jianzong Wu, Chao Tang, et al. An empirical study of gpt-4o image generation capabilities. arXiv preprint arXiv:2504.05979, 2025

2025 arXiv
[26]

Manga generation via layout-controllable diffusion

Siyu Chen, Dengjie Li, Zenghao Bao, Yao Zhou, Lingfeng Tan, Yujie Zhong, and Zheng Zhao. Manga generation via layout-controllable diffusion. In arXiv preprint arxiv:2412.19303, 2024

2024 arXiv
[27]

All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss

Wei-Ting Chen, Hao-Yu Fang, Cheng-Lin Hsieh, Cheng-Che Tsai, I Chen, Jian-Jiun Ding, Sy-Yen Kuo, et al. All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. In Proceedings of the IEEE/CVF in...

2021
[28]

Dreamidentity: Improved editability for efficient face-identity preserved image generation

Zhuowei Chen, Shancheng Fang, Wei Liu, Qian He, Mengqi Huang, Yongdong Zhang, and Zhendong Mao. Dreamidentity: Improved editability for efficient face-identity preserved image generation. arXiv preprint arXiv:2307.00300, 2023

2023 arXiv
[29]

Spatialrgpt: Grounded spatial reasoning in vision language models

An-Chieh Cheng, Hongxu Yin, Yang Fu, Qiushan Guo, Ruihan Yang, Jan Kautz, Xiaolong Wang, and Sifei Liu. Spatialrgpt: Grounded spatial reasoning in vision language models. arXiv preprint arXiv:2406.01584, 2024

2024 arXiv
[30]

Snow mask guided adaptive residual network for image snow removal

Bodong Cheng, Juncheng Li, Ying Chen, and Tieyong Zeng. Snow mask guided adaptive residual network for image snow removal. Computer Vision and Image Understanding , 236:103819, 2023

2023
[31]

Masked-attention mask transformer for universal image segmentation

Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1290–1299, 2022

2022
[32]

Per-pixel classification is not all you need for semantic segmentation

Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. Advances in neural information processing systems , 34:17864–17875, 2021. 208

2021
[33]

Object counting and instance segmentation with image-level supervision

Hisham Cholakkal, Guolei Sun, Fahad Shahbaz Khan, and Ling Shao. Object counting and instance segmentation with image-level supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12397–12405, 2019

2019
[34]

Generating diverse agricultural data for vision-based farming applications

Mikolaj Cieslak, Umabharathi Govindarajan, Alejandro Garcia, Anuradha Chandrashekar, Torsten Hadrich, Aleksander Mendoza-Drosik, Dominik L Michels, Soren Pirk, Chia-Chun Fu, and Wojciech Palubicki. Generating diverse agricultural data for vision-based farming applications. In ...

2024
[35]

The cityscapes dataset for semantic urban scene understanding

Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognitio...

2016
[36]

Latentpaint: Image inpainting in latent space with diffusion models

Ciprian Corneanu, Raghudeep Gadde, and Aleix M Martinez. Latentpaint: Image inpainting in latent space with diffusion models. pages 4334–4343, 2024

2024
[37]

Deep learning based 2d human pose estimation: A survey

Qi Dang, Jianqin Yin, Bin Wang, and Wenqing Zheng. Deep learning based 2d human pose estimation: A survey. Tsinghua Science and Technology, 24(6):663–676, 2019

2019
[38]

3d-aware conditional image synthesis

Kangle Deng, Gengshan Yang, Deva Ramanan, and Jun-Yan Zhu. 3d-aware conditional image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4434–4445, 2023

2023
[39]

Towards intelligent design: A self-driven framework for collocated clothing synthesis leveraging fashion styles and textures

Minglong Dong, Dongliang Zhou, Jianghong Ma, and Haijun Zhang. Towards intelligent design: A self-driven framework for collocated clothing synthesis leveraging fashion styles and textures. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Proces...

2024
[40]

Dreamartist: Towards controllable one-shot text-to- image generation via contrastive prompt-tuning

Ziyi Dong, Pengxu Wei, and Liang Lin. Dreamartist: Towards controllable one-shot text-to- image generation via contrastive prompt-tuning. arXiv preprint arXiv:2211.11337, 2022

2022 arXiv
[41]

Discovering novel biological traits from images using phylogeny-guided neural networks

Mohannad Elhamod, Mridul Khurana, Harish Babu Manogaran, Josef C Uyeda, Meghan A Balk, Wasila Dahdul, Yasin Bakis, Henry L Bart Jr, Paula M Mabee, Hilmar Lapp, et al. Discovering novel biological traits from images using phylogeny-guided neural networks. In Proceedings of the ...

2023
[42]

Everingham, L

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascal- network.org/challenges/VOC/voc2007/workshop/index.html

2007
[43]

Everingham, L

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal- network.org/challenges/VOC/voc2012/workshop/index.html

2012
[44]

Guid- ing instruction-based image editing via multimodal large language models

Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, and Zhe Gan. Guid- ing instruction-based image editing via multimodal large language models. arXiv preprint arXiv:2309.17102, 2023

2023 arXiv
[45]

An image is worth one word: Personalizing text-to-image generation using textual inversion

Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022

2022 arXiv
[46]

Cascadedgaze: Efficiency in global context extraction for image restoration

Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh, Chunhua Zhou, Fengyu Sun, and Di Niu. Cascadedgaze: Efficiency in global context extraction for image restoration. arXiv preprint arXiv:2401.15235, 2024

2024 arXiv
[47]

Fast r-cnn

Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448, 2015. 209

2015
[48]

Rich feature hierarchies for accurate object detection and semantic segmentation

Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580–587, 2014

2014
[49]

Talecrafter: Interactive story visualization with multiple characters, 2023

Yuan Gong, Youxin Pang, Xiaodong Cun, Menghan Xia, Yingqing He, Haoxin Chen, Longyue Wang, Yong Zhang, Xintao Wang, Ying Shan, and Yujiu Yang. Talecrafter: Interactive story visualization with multiple characters, 2023

2023
[50]

Dart: Denoising autoregressive transformer for scalable text-to-image generation

Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, and Shuangfei Zhai. Dart: Denoising autoregressive transformer for scalable text-to-image generation. arXiv preprint arXiv:2410.08159, 2024

arXiv
[51]

Modulating pretrained diffusion models for multimodal image synthesis

Cusuh Ham, James Hays, Jingwan Lu, Krishna Kumar Singh, Zhifei Zhang, and Tobias Hinz. Modulating pretrained diffusion models for multimodal image synthesis. arXiv preprint arXiv:2302.12764, 2023

2023 arXiv
[52]

Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis

Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. arXiv preprint arXiv:2412.04431, 2024

2024 arXiv
[53]

Xintong Han, Ying Jin, Jing Zhang, Qiulei Dong, and Larry S. Davis. Viton-hd: High- resolution virtual try-on via misalignment-aware normalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14131– 14140, 2021

2021
[54]

Xintong Han, Zuxuan Wu, Zhe Wu, Ruichi Yu, and Larry S. Davis. Viton: An image-based virtual try-on network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 7543–7552, 2018

2018
[55]

Dreamstory: Open-domain story visualization by llm-guided multi-subject consistent diffusion, 2025

Huiguo He, Huan Yang, Zixi Tuo, Yuan Zhou, Qiuyue Wang, Yuhang Zhang, Zeyu Liu, Wenhao Huang, Hongyang Chao, and Jian Yin. Dreamstory: Open-domain story visualization by llm-guided multi-subject consistent diffusion, 2025

2025
[56]

Styleposegan: Pose-consistent virtual try-on via pose-guided style transfer

Jiawei He, Yunchao Wei, and Yi Yang. Styleposegan: Pose-consistent virtual try-on via pose-guided style transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(11):8110–8124, 2022

2022
[57]

Dresscode: Au- toregressively sewing and generating garments from text guidance

Kai He, Kaixin Yao, Qixuan Zhang, Jingyi Yu, Lingjie Liu, and Lan Xu. Dresscode: Au- toregressively sewing and generating garments from text guidance. ACM Transactions on Graphics (TOG), 43(4):1–13, 2024

2024
[58]

Synthset: Gen- erative diffusion model for semantic segmentation in precision agriculture

Andrew Heschl, Mauricio Murillo, Keyhan Najafian, and Farhad Maleki. Synthset: Gen- erative diffusion model for semantic segmentation in precision agriculture. arXiv preprint arXiv:2411.03505, 2024

2024 arXiv
[59]

Cocktail: Mixing multi-modality controls for text-conditional image generation

Minghui Hu, Jianbin Zheng, Daqing Liu, Chuanxia Zheng, Chaoyue Wang, Dacheng Tao, and Tat-Jen Cham. Cocktail: Mixing multi-modality controls for text-conditional image generation. arXiv preprint arXiv:2306.00964, 2023

2023 arXiv
[60]

Com- poser: Creative and controllable image synthesis with composable conditions

Lianghua Huang, Di Chen, Yu Liu, Yujun Shen, Deli Zhao, and Jingren Zhou. Com- poser: Creative and controllable image synthesis with composable conditions. arXiv preprint arXiv:2302.09778, 2023

2023 arXiv
[61]

Learning disentangled identifiers for action-customized text-to-image generation

Siteng Huang, Biao Gong, Yutong Feng, Xi Chen, Yuqian Fu, Yu Liu, and Donglin Wang. Learning disentangled identifiers for action-customized text-to-image generation. arXiv preprint arXiv:2311.15841, 2023

2023 arXiv
[62]

Autogeo: Automating geometric image dataset creation for enhanced geometry understanding

Zihan Huang, Tao Wu, Wang Lin, Shengyu Zhang, Jingyuan Chen, and Fei Wu. Autogeo: Automating geometric image dataset creation for enhanced geometry understanding. arXiv preprint arXiv:2409.09039, 2024

2024 arXiv
[63]

Reversion: Diffusion-based relation inversion from images

Ziqi Huang, Tianxing Wu, Yuming Jiang, Kelvin CK Chan, and Ziwei Liu. Reversion: Diffusion-based relation inversion from images. arXiv preprint arXiv:2303.13495, 2023. 210

2023 arXiv
[64]

Liteflownet: A lightweight convolutional neural network for optical flow estimation

Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. Liteflownet: A lightweight convolutional neural network for optical flow estimation. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 8981–8989, 2018

2018
[65]

Vision transformer in industrial visual inspection

Nils Hütten, Richard Meyes, and Tobias Meisen. Vision transformer in industrial visual inspection. Applied Sciences, 12(23):11981, 2022

2022
[66]

Flownet 2.0: Evolution of optical flow estimation with deep networks

Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox. Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2462–2470, 2017

2017
[67]

Desnowgan: An efficient single image snow removal framework using cross-resolution lateral connection and gans

Da-Wei Jaw, Shih-Chia Huang, and Sy-Yen Kuo. Desnowgan: An efficient single image snow removal framework using cross-resolution lateral connection and gans. IEEE Transactions on Circuits and Systems for Video Technology, 31(4):1342–1350, 2020

2020
[68]

Remote sensing change detection in urban environments

John R Jensen and Jungho Im. Remote sensing change detection in urban environments. Geo-spatial Technologies in Urban Environments: Policy, Practice, and Pixels, pages 7–31, 2007

2007
[69]

Culture-trip: Culturally-aware text-to-image generation with iterative prompt refinment

Suchae Jeong, Inseong Choi, Youngsik Yun, and Jihie Kim. Culture-trip: Culturally-aware text-to-image generation with iterative prompt refinment. arXiv preprint arXiv:2502.16902, 2025

2025 arXiv
[70]

Ssmg: Spatial-semantic map guided diffusion model for free-form layout-to-image generation

Chengyou Jia, Minnan Luo, Zhuohang Dang, Guang Dai, Xiaojun Chang, Mengmeng Wang, and Jingdong Wang. Ssmg: Spatial-semantic map guided diffusion model for free-form layout-to-image generation. arXiv preprint arXiv:2308.10156, 2023

2023 arXiv
[71]

Lumen: Unleashing versatile vision-centric capabilities of large multimodal models

Yang Jiao, Shaoxiang Chen, Zequn Jie, Jingjing Chen, Lin Ma, and Yu-Gang Jiang. Lumen: Unleashing versatile vision-centric capabilities of large multimodal models. arXiv preprint arXiv:2403.07304, 2024

2024 arXiv
[72]

Beyond aesthetics: Cultural competence in text-to-image models

Nithish Kannen, Arif Ahmad, Marco Andreetto, Vinodkumar Prabhakaran, Utsav Prabhu, Adji Bousso Dieng, Pushpak Bhattacharyya, and Shachi Dave. Beyond aesthetics: Cultural competence in text-to-image models. arXiv preprint arXiv:2407.06863, 2024

2024 arXiv
[73]

3d gaussian splatting for real-time radiance field rendering

Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1, 2023

2023
[74]

Diff- blender: Scalable and composable multimodal text-to-image diffusion models

Sungnyun Kim, Junsoo Lee, Kibeom Hong, Daesik Kim, and Namhyuk Ahn. Diff- blender: Scalable and composable multimodal text-to-image diffusion models. arXiv preprint arXiv:2305.15194, 2023

2023 arXiv
[75]

Probabilistic modeling for human mesh recovery

Nikos Kolotouros, Georgios Pavlakos, Dinesh Jayaraman, and Kostas Daniilidis. Probabilistic modeling for human mesh recovery. In Proceedings of the IEEE/CVF international conference on computer vision, pages 11605–11614, 2021

2021
[76]

Raindrop-removal image translation using target-mask network with attention module

Hyuk-Ju Kwon and Sung-Hak Lee. Raindrop-removal image translation using target-mask network with attention module. Mathematics, 11(15):3318, 2023

2023
[77]

Dicti: Diffusion-based clothing designer via text-guided input

Ajda Lampe, Julija Stopar, Deepak K Jain, Shinichiro Omachi, Peter Peer, and Vitomir Štruc. Dicti: Diffusion-based clothing designer via text-guided input. In 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pages 1–9. IEEE, 2024

2024
[78]

Physics-based shadow image decomposition for shadow removal

Hieu Le and Dimitris Samaras. Physics-based shadow image decomposition for shadow removal. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12):9088–9101, 2021

2021
[79]

From text to visuals: Using llms to generate math diagrams with vector graphics

Jaewook Lee, Jeongah Lee, Wanyong Feng, and Andrew Lan. From text to visuals: Using llms to generate math diagrams with vector graphics. arXiv preprint arXiv:2503.07429, 2025

2025 arXiv
[80]

An underwater image enhancement benchmark dataset and beyond

Chongyi Li, Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, and Dacheng Tao. An underwater image enhancement benchmark dataset and beyond. IEEE transactions on image processing, 29:4376–4389, 2019. 211

2019
[81]

Real-world deep local motion deblurring

Haoying Li, Ziran Zhang, Tingting Jiang, Peng Luo, Huajun Feng, and Zhihai Xu. Real-world deep local motion deblurring. In proceedings of the AAAI conference on artificial intelligence, volume 37, pages 1314–1322, 2023

2023
[82]

Cheffusion: Multimodal foundation model integrating recipe and food image generation

Peiyu Li, Xiaobao Huang, Yijun Tian, and Nitesh V Chawla. Cheffusion: Multimodal foundation model integrating recipe and food image generation. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pages 3872–3876, 2024

2024
[83]

Image content generation with causal reasoning

Xiaochuan Li, Baoyu Fan, Runze Zhang, Liang Jin, Di Wang, Zhenhua Guo, Yaqian Zhao, and Rengang Li. Image content generation with causal reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 13646–13654, 2024

2024
[84]

Exploiting reflection change for automatic reflection removal

Yu Li and Michael S Brown. Exploiting reflection change for automatic reflection removal. In Proceedings of the IEEE international conference on computer vision, pages 2432–2439, 2013

2013
[85]

Generate anything anywhere in any scene

Yuheng Li, Haotian Liu, Yangming Wen, and Yong Jae Lee. Generate anything anywhere in any scene. arXiv preprint arXiv:2306.17154, 2023

2023 arXiv
[86]

Gligen: Open-set grounded text-to-image generation

Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22511–22521, 2023

2023
[87]

Swinir: Image restoration using swin transformer

Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF interna- tional conference on computer vision, pages 1833–1844, 2021

2021
[88]

Object counting: You only need to look at one

Hui Lin, Xiaopeng Hong, and Yabin Wang. Object counting: You only need to look at one. arXiv preprint arXiv:2112.05993, 2021

2021 arXiv
[89]

Phys4dgen: A physics- driven framework for controllable and efficient 4d content generation from a single image

Jiajing Lin, Zhenzhong Wang, Shu Jiang, Yongjie Hou, and Min Jiang. Phys4dgen: A physics- driven framework for controllable and efficient 4d content generation from a single image. arXiv preprint arXiv:2411.16800, 2024

2024
[90]

Microsoft coco: Common objects in context

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer vision–ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings...

2014
[91]

On the cultural gap in text-to-image generation

Bingshuai Liu, Longyue Wang, Chenyang Lyu, Yong Zhang, Jinsong Su, Shuming Shi, and Zhaopeng Tu. On the cultural gap in text-to-image generation. In ECAI 2024, pages 930–937. IOS Press, 2024

2024
[92]

Generative physical ai in vision: A survey

Daochang Liu, Junyu Zhang, Anh-Dung Dinh, Eunbyung Park, Shichao Zhang, and Chang Xu. Generative physical ai in vision: A survey. arXiv preprint arXiv:2501.10928, 2025

2025 arXiv
[93]

Stylecrafter: Enhancing stylized text-to-video generation with style adapter

Gongye Liu, Menghan Xia, Yong Zhang, Haoxin Chen, Jinbo Xing, Xintao Wang, Yujiu Yang, and Ying Shan. Stylecrafter: Enhancing stylized text-to-video generation with style adapter. arXiv preprint arXiv:2312.00330, 2023

2023 arXiv
[94]

Structure matters: Tackling the semantic discrepancy in diffusion models for image inpainting

Haipeng Liu, Yang Wang, Biao Qian, Meng Wang, and Yong Rui. Structure matters: Tackling the semantic discrepancy in diffusion models for image inpainting. pages 8038–8047, 2024

2024
[95]

One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization

Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization. Advances in Neural Information Processing Systems, 36:22226–22246, 2023

2023
[96]

Git-mol: A multi-modal large language model for molecular science with graph, image, and text

Pengfei Liu, Yiming Ren, Jun Tao, and Zhixiang Ren. Git-mol: A multi-modal large language model for molecular science with graph, image, and text. Computers in biology and medicine, 171:108073, 2024. 212

2024
[97]

Character-aware models improve visual text rendering

Rosanne Liu, Dan Garrette, Chitwan Saharia, William Chan, Adam Roberts, Sharan Narang, Irina Blok, RJ Mical, Mohammad Norouzi, and Noah Constant. Character-aware models improve visual text rendering. arXiv preprint arXiv:2212.10562, 2022

2022 arXiv
[98]

Zero-1-to-3: Zero-shot one image to 3d object

Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl V ondrick. Zero-1-to-3: Zero-shot one image to 3d object. InProceedings of the IEEE/CVF international conference on computer vision, pages 9298–9309, 2023

2023
[99]

Physgen: Rigid-body physics-grounded image-to-video generation

Shaowei Liu, Zhongzheng Ren, Saurabh Gupta, and Shenlong Wang. Physgen: Rigid-body physics-grounded image-to-video generation. In European Conference on Computer Vision, pages 360–378. Springer, 2024

2024
[100]

Ssd: Single shot multibox detector

Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, p...

2016
[101]

From synthetic to real: Image dehazing collaborating with unlabeled real data

Ye Liu, Lei Zhu, Shunda Pei, Huazhu Fu, Jing Qin, Qing Zhang, Liang Wan, and Wei Feng. From synthetic to real: Image dehazing collaborating with unlabeled real data. In Proceedings of the 29th ACM international conference on multimedia, pages 50–58, 2021

2021
[102]

Application of ghost- deblurgan to fiducial marker detection

Yibo Liu, Amaldev Haridevan, Hunter Schofield, and Jinjun Shan. Application of ghost- deblurgan to fiducial marker detection. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6827–6832. IEEE, 2022

2022
[103]

Cones: Concept neurons in diffusion models for customized generation

Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao. Cones: Concept neurons in diffusion models for customized generation. arXiv preprint arXiv:2303.05125, 2023

2023 arXiv
[104]

Image anything: Towards reasoning-coherent and training-free multi-modal image generation

Yuanhuiyi Lyu, Xu Zheng, and Lin Wang. Image anything: Towards reasoning-coherent and training-free multi-modal image generation. arXiv preprint arXiv:2401.17664, 2024

2024 arXiv
[105]

Glyphdraw: Learning to draw chinese characters in image synthesis models coherently

Jian Ma, Mingjun Zhao, Chen Chen, Ruichen Wang, Di Niu, Haonan Lu, and Xiaodong Lin. Glyphdraw: Learning to draw chinese characters in image synthesis models coherently. arXiv preprint arXiv:2303.17870, 2023

2023 arXiv
[106]

Bayesian image reconstruction using deep generative models

Razvan V Marinescu, Daniel Moyer, and Polina Golland. Bayesian image reconstruction using deep generative models. arXiv preprint arXiv:2012.04567, 2020

2012 arXiv
[107]

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings eighth IEEE international conference on computer vision. IC...

2001
[108]

Phybench: A physical commonsense benchmark for evaluating text-to-image models

Fanqing Meng, Wenqi Shao, Lixin Luo, Yahong Wang, Yiran Chen, Quanfeng Lu, Yue Yang, Tianshuo Yang, Kaipeng Zhang, Yu Qiao, et al. Phybench: A physical commonsense benchmark for evaluating text-to-image models. arXiv preprint arXiv:2406.11802, 2024

2024 arXiv
[109]

Single image depth estimation: An overview

Alican Mertan, Damien Jade Duff, and Gozde Unal. Single image depth estimation: An overview. Digital Signal Processing, 123:103441, 2022

2022
[110]

Nerf: Representing scenes as neural radiance fields for view synthesis

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoor- thi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99–106, 2021

2021
[111]

Enhancing weed detection performance by means of genai-based image augmentation

Sourav Modak and Anthony Stein. Enhancing weed detection performance by means of genai-based image augmentation. arXiv preprint arXiv:2411.18513, 2024

2024 arXiv
[112]

Generative ai-based pipeline architecture for increasing training efficiency in intelligent weed control systems

Sourav Modak and Anthony Stein. Generative ai-based pipeline architecture for increasing training efficiency in intelligent weed control systems. arXiv preprint arXiv:2411.00548, 2024

2024 arXiv
[113]

Lego: Learning to disentangle and invert concepts beyond object appearance in text-to-image diffusion models

Saman Motamed, Danda Pani Paudel, and Luc Van Gool. Lego: Learning to disentangle and invert concepts beyond object appearance in text-to-image diffusion models. arXiv preprint arXiv:2311.13833, 2023. 213

2023 arXiv
[114]

T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453, 2023

2023 arXiv
[115]

Deep multi-scale convolutional neural network for dynamic scene deblurring

Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883–3891, 2017

2017
[116]

Image colorization using generative adversar- ial networks

Kamyar Nazeri, Eric Ng, and Mehran Ebrahimi. Image colorization using generative adversar- ial networks. In Articulated Motion and Deformable Objects: 10th International Conference, AMDO 2018, Palma de Mallorca, Spain, July 12-13, 2018, Proceedings 10 , pages 85–94. Springer, 2018

2018
[117]

Glide: Towards photorealistic image generation and editing with text-guided diffusion models

Alexander Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021

2021 arXiv
[118]

Towards perspective-free object counting with deep learning

Daniel Onoro-Rubio and Roberto J López-Sastre. Towards perspective-free object counting with deep learning. In European conference on computer vision, pages 615–629. Springer, 2016

2016
[119]

Human mesh recovery from multiple shots

Georgios Pavlakos, Jitendra Malik, and Angjoo Kanazawa. Human mesh recovery from multiple shots. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1485–1495, 2022

2022
[120]

Dress code: High-resolution multi-modal fashion image translation

Lorenzo Pisanello, Riccardo Spezialetti, Barbara Bruno, and Fulvio Mastrogiovanni. Dress code: High-resolution multi-modal fashion image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12243–12252, 2020

2020
[121]

Sdxl: Improving latent diffusion models for high-resolution image synthesis

Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023

2023 arXiv
[122]

Dialogue with the machine and dialogue with the art world: Evaluating generative ai for culturally-situated creativity

Rida Qadri, Piotr Mirowski, Aroussiak Gabriellan, Farbod Mehr, Huma Gupta, Pamela Karimi, and Remi Denton. Dialogue with the machine and dialogue with the art world: Evaluating generative ai for culturally-situated creativity. arXiv preprint arXiv:2412.14077, 2024

2024 arXiv
[123]

Geonet: Geometric neural network for joint depth and surface normal estimation

Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, and Jiaya Jia. Geonet: Geometric neural network for joint depth and surface normal estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 283–291, 2018

2018
[124]

Attentive generative adversarial network for raindrop removal from a single image

Rui Qian, Robby T Tan, Wenhan Yang, Jiajun Su, and Jiaying Liu. Attentive generative adversarial network for raindrop removal from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2482–2491, 2018

2018
[125]

Molscribe: robust molecular structure recognition with image-to-graph generation

Yujie Qian, Jiang Guo, Zhengkai Tu, Zhening Li, Connor W Coley, and Regina Barzilay. Molscribe: robust molecular structure recognition with image-to-graph generation. Journal of Chemical Information and Modeling, 63(7):1925–1934, 2023

1925
[126]

Unicontrol: A unified diffusion model for controllable visual generation in the wild

Can Qin, Shu Zhang, Ning Yu, Yihao Feng, Xinyi Yang, Yingbo Zhou, Huan Wang, Juan Carlos Niebles, Caiming Xiong, Silvio Savarese, et al. Unicontrol: A unified diffusion model for controllable visual generation in the wild. arXiv preprint arXiv:2305.11147, 2023

2023 arXiv
[127]

Nasnet: A neuron attention stage-by-stage net for single image deraining

Xu Qin and Zhilin Wang. Nasnet: A neuron attention stage-by-stage net for single image deraining. arXiv preprint arXiv:1912.03151, 2019

1912 arXiv
[128]

Ffa-net: Feature fusion attention network for single image dehazing

Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. Ffa-net: Feature fusion attention network for single image dehazing. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 11908–11915, 2020

2020
[129]

Deshad- ownet: A multi-context embedding deep network for shadow removal

Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, and Rynson WH Lau. Deshad- ownet: A multi-context embedding deep network for shadow removal. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4067–4075, 2017. 214

2017
[130]

Removing raindrops and rain streaks in one go

Ruijie Quan, Xin Yu, Yuanzhi Liang, and Yi Yang. Removing raindrops and rain streaks in one go. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9147–9156, 2021

2021
[131]

Gaussian kernel mixture network for single image defocus deblurring

Yuhui Quan, Zicong Wu, and Hui Ji. Gaussian kernel mixture network for single image defocus deblurring. Advances in Neural Information Processing Systems, 34:20812–20824, 2021

2021
[132]

Deep single image defocus deblurring via gaussian kernel mixture learning

Yuhui Quan, Zicong Wu, Ruotao Xu, and Hui Ji. Deep single image defocus deblurring via gaussian kernel mixture learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

2024
[133]

Hierarchical text-conditional image generation with clip latents

Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022

2022 arXiv
[134]

Optical flow estimation using a spatial pyramid network

Anurag Ranjan and Michael J Black. Optical flow estimation using a spatial pyramid network. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4161–4170, 2017

2017
[135]

Underwater image enhancement: a comprehensive review, recent trends, challenges and applications

Smitha Raveendran, Mukesh D Patil, and Gajanan K Birajdar. Underwater image enhancement: a comprehensive review, recent trends, challenges and applications. Artificial Intelligence Review, 54:5413–5467, 2021

2021
[136]

Kandinsky: an improved text-to-image synthesis with image prior and latent diffusion

Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Maltseva, Vladimir Arkhipkin, Igor Pavlov, Ilya Ryabov, Angelina Kuts, Alexander Panchenko, Andrey Kuznetsov, and Denis Dimitrov. Kandinsky: an improved text-to-image synthesis with image prior and latent diffusion. arXiv preprin...

arXiv
[137]

Faster r-cnn: Towards real-time object detection with region proposal networks

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015

2015
[138]

Real-world blur dataset for learning and benchmarking deblurring algorithms

Jaesung Rim, Haeyun Lee, Jucheol Won, and Sunghyun Cho. Real-world blur dataset for learning and benchmarking deblurring algorithms. In Computer vision–ECCV 2020: 16th European conference, glasgow, UK, August 23–28, 2020, proceedings, part XXV 16, pages 184–201. Springer, 2020

2020
[139]

High-resolution image synthesis with latent diffusion models

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022

2022
[140]

U-net: Convolutional networks for biomedical image segmentation

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part ...

2015
[141]

GAN with skip patch discriminator for biological electron microscopy image generation

Nishith Ranjon Roy. GAN with skip patch discriminator for biological electron microscopy image generation. University of Arkansas, 2024

2024
[142]

Devil in the details: Towards accurate single and multiple human parsing

Tao Ruan, Ting Liu, Zilong Huang, Yunchao Wei, Shikui Wei, and Yao Zhao. Devil in the details: Towards accurate single and multiple human parsing. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 4814–4821, 2019

2019
[143]

Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aber- man. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 225...

2023
[144]

Photorealistic text-to-image diffusion models with deep language understanding

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. Photorealistic text-to-image diffusion models with deep language understanding. ...

2022
[145]

Generative ai in agriculture: Creating image datasets using dall

Ranjan Sapkota and Manoj Karkee. Generative ai in agriculture: Creating image datasets using dall. e’s advanced large language model capabilities. arXiv preprint arXiv:2307.08789, 2023

2023 arXiv
[146]

Learning human mesh recovery in 3d scenes

Zehong Shen, Zhi Cen, Sida Peng, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Learning human mesh recovery in 3d scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17038–17047, 2023

2023
[147]

Fashionsd-x: Multimodal fashion garment synthesis using latent diffusion

Abhishek Kumar Singh and Ioannis Patras. Fashionsd-x: Multimodal fashion garment synthesis using latent diffusion. arXiv preprint arXiv:2404.18591, 2024

2024 arXiv
[148]

Continual diffusion: Continual customization of text-to-image diffusion with c-lora

James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, and Hongxia Jin. Continual diffusion: Continual customization of text-to-image diffusion with c-lora. arXiv preprint arXiv:2304.06027, 2023

2023 arXiv
[149]

Styledrop: Text-to-image generation in any style

Kihyuk Sohn, Nataniel Ruiz, Kimin Lee, Daniel Castro Chin, Irina Blok, Huiwen Chang, Jarred Barber, Lu Jiang, Glenn Entis, Yuanzhen Li, et al. Styledrop: Text-to-image generation in any style. arXiv preprint arXiv:2306.00983, 2023

2023 arXiv
[150]

A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects

Kechen Song and Yunhui Yan. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Applied Surface Science, 285:858–864, 2013

2013
[151]

Physicsgen: Can generative models learn from images to predict complex physical relations? arXiv preprint arXiv:2503.05333, 2025

Martin Spitznagel, Jan Vaillant, and Janis Keuper. Physicsgen: Can generative models learn from images to predict complex physical relations? arXiv preprint arXiv:2503.05333, 2025

2025 arXiv
[152]

Wordrobe: Text-guided generation of textured 3d garments

Astitva Srivastava, Pranav Manu, Amit Raj, Varun Jampani, and Avinash Sharma. Wordrobe: Text-guided generation of textured 3d garments. In European Conference on Computer Vision, pages 458–475. Springer, 2024

2024
[153]

Dreamgaussian: Generative gaussian splatting for efficient 3d content creation

Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309.16653, 2023

2023 arXiv
[154]

Jacobs, Bharath Hariharan, Yael Pritch, Neal Wadhwa, Kfir Aberman, and Michael Rubinstein

Luming Tang, Nataniel Ruiz, Qinghao Chu, Yuanzhen Li, Aleksander Holynski, David E. Jacobs, Bharath Hariharan, Yael Pritch, Neal Wadhwa, Kfir Aberman, and Michael Rubinstein. Realfill: Reference-driven generation for authentic image completion. ACM Transactions on Graphics, 43...

2024
[155]

Online pcb defect detector on a new pcb defect dataset, 2019

Sanli Tang, Fan He, Xiaolin Huang, and Jie Yang. Online pcb defect detector on a new pcb defect dataset, 2019

2019
[156]

What do single-view 3d reconstruction networks learn? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3405–3414, 2019

Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3405–3414, 2019

2019
[157]

Real-time industrial visual inspection: A review

Andrew DH Thomas, Michael G Rodd, John D Holt, and CJ Neill. Real-time industrial visual inspection: A review. Real-Time Imaging, 1(2):139–158, 1995

1995
[158]

Triposr: Fast 3d object reconstruction from a single image

Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao. Triposr: Fast 3d object reconstruction from a single image. arXiv preprint arXiv:2403.02151, 2024

2024 arXiv
[159]

Jetformer: An au- toregressive generative model of raw images and text

Michael Tschannen, André Susano Pinto, and Alexander Kolesnikov. Jetformer: An au- toregressive generative model of raw images and text. arXiv preprint arXiv:2411.19722 , 2024

2024 arXiv
[160]

Consistent view synthesis with pose-guided diffusion models

Hung-Yu Tseng, Qinbo Li, Changil Kim, Suhib Alsisan, Jia-Bin Huang, and Johannes Kopf. Consistent view synthesis with pose-guided diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16773–16783, 2023

2023
[161]

Anytext: Multilingual visual text generation and editing

Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng, and Xuansong Xie. Anytext: Multilingual visual text generation and editing. arXiv preprint arXiv:2311.03054, 2023

2023 arXiv
[162]

Face0: Instantaneously conditioning a text-to-image model on a face

Dani Valevski, Danny Lumen, Yossi Matias, and Yaniv Leviathan. Face0: Instantaneously conditioning a text-to-image model on a face. In SIGGRAPH Asia 2023 Conference Papers, pages 1–10, 2023. 216

2023
[163]

arXiv preprint arXiv:2303.09522, 2023

Andrey V oynov, Qinghao Chu, Daniel Cohen-Or, and Kfir Aberman.p+: Extended textual conditioning in text-to-image generation. arXiv preprint arXiv:2303.09522, 2023

2023 arXiv
[164]

Old photo restoration via deep latent space translation

Ziyu Wan, Bo Zhang, Dong Chen, Pan Zhang, Fang Wen, and Jing Liao. Old photo restoration via deep latent space translation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):2071–2087, 2022

2022
[165]

Towards characteristic-preserving image-based virtual try-on network

Bochao Wang, Huabin Zheng, Xiaodan Liang, Yimin Chen, Liang Lin, and Meng Yang. Towards characteristic-preserving image-based virtual try-on network. In Proceedings of the European Conference on Computer Vision (ECCV), pages 589–604, 2018

2018
[166]

Is a picture worth a thousand words? delving into spatial reasoning for vision language models

Jiayu Wang, Yifei Ming, Zhenmei Shi, Vibhav Vineet, Xin Wang, Sharon Li, and Neel Joshi. Is a picture worth a thousand words? delving into spatial reasoning for vision language models. Advances in Neural Information Processing Systems, 37:75392–75421, 2024

2024
[167]

Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal

Jifeng Wang, Xiang Li, and Jian Yang. Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1788–1797, 2018

2018
[168]

Deep 3d human pose estimation: A review

Jinbao Wang, Shujie Tan, Xiantong Zhen, Shuo Xu, Feng Zheng, Zhenyu He, and Ling Shao. Deep 3d human pose estimation: A review. Computer Vision and Image Understanding , 210:103225, 2021

2021
[169]

Magicgeo: Training- free text-guided geometric diagram generation

Junxiao Wang, Ting Zhang, Heng Yu, Jingdong Wang, and Hua Huang. Magicgeo: Training- free text-guided geometric diagram generation. arXiv preprint arXiv:2502.13855, 2025

2025 arXiv
[170]

Designing deep networks for surface normal estimation

Xiaolong Wang, David Fouhey, and Abhinav Gupta. Designing deep networks for surface normal estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 539–547, 2015

2015
[171]

Emu3: Next-token prediction is all you need

Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024

2024 arXiv
[172]

Esrgan: Enhanced super-resolution generative adversarial networks

Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. Esrgan: Enhanced super-resolution generative adversarial networks. In Pro- ceedings of the European conference on computer vision (ECCV) workshops , pages 0–0, 2018

2018
[173]

Lite pose: Efficient architecture design for 2d human pose estimation

Yihan Wang, Muyang Li, Han Cai, Wei-Ming Chen, and Song Han. Lite pose: Efficient architecture design for 2d human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13126–13136, 2022

2022
[174]

Cook- ingdiffusion: Cooking procedural image generation with stable diffusion

Yuan Wang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Yi Tan, and Xiang Wang. Cook- ingdiffusion: Cooking procedural image generation with stable diffusion. arXiv preprint arXiv:2501.09042, 2025

2025 arXiv
[175]

Crm: Single image to 3d textured mesh with convolutional reconstruction model

Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, and Jun Zhu. Crm: Single image to 3d textured mesh with convolutional reconstruction model. In European Conference on Computer Vision, pages 57–74. Springer, 2024

2024
[176]

Novel view synthesis with diffusion models

Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. arXiv preprint arXiv:2210.04628, 2022

2022 arXiv
[177]

Fastdepth: Fast monocular depth estimation on embedded systems

Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, and Vivienne Sze. Fastdepth: Fast monocular depth estimation on embedded systems. In 2019 International Conference on Robotics and Automation (ICRA), pages 6101–6108. IEEE, 2019

2019
[178]

Contrastive learning for compact single image dehazing

Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. Contrastive learning for compact single image dehazing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10551–10560, 2021. 217

2021
[179]

Diffsensei: Bridging multi-modal llms and diffusion models for customized manga generation

Jianzong Wu, Chao Tang, Jingbo Wang, Yanhong Zeng, Xiangtai Li, and Yunhai Tong. Diffsensei: Bridging multi-modal llms and diffusion models for customized manga generation. arXiv preprint arXiv:2412.07589, 2024

2024 arXiv
[180]

Dota: A large-scale dataset for object detection in aerial images

Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang. Dota: A large-scale dataset for object detection in aerial images. In Proceedings of the IEEE conference on computer vision and pattern recognition, page...

2018
[181]

Gsva: Generalized segmentation via multimodal large language models

Zhuofan Xia, Dongchen Han, Yizeng Han, Xuran Pan, Shiji Song, and Gao Huang. Gsva: Generalized segmentation via multimodal large language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3858–3869, 2024

2024
[182]

Fastcom- poser: Tuning-free multi-subject image generation with localized attention

Guangxuan Xiao, Tianwei Yin, William T Freeman, Frédo Durand, and Song Han. Fastcom- poser: Tuning-free multi-subject image generation with localized attention. arXiv preprint arXiv:2305.10431, 2023

2023 arXiv
[183]

Comcat: Towards efficient compression and customization of attention-based vision models

Jinqi Xiao, Miao Yin, Yu Gong, Xiao Zang, Jian Ren, and Bo Yuan. Comcat: Towards efficient compression and customization of attention-based vision models. arXiv preprint arXiv:2305.17235, 2023

2023 arXiv
[184]

Can generative ai replace immunofluorescent staining processes? a comparison study of synthet- ically generated cellpainting images from brightfield

Xiaodan Xing, Siofra Murdoch, Chunling Tang, Giorgos Papanastasiou, Jan Cross-Zamirski, Yunzhe Guo, Xianglu Xiao, Carola-Bibiane Schönlieb, Yinhai Wang, and Guang Yang. Can generative ai replace immunofluorescent staining processes? a comparison study of synthet- ically genera...

2024
[185]

Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation

Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. In European Conference on Computer Vision, pages 1–20. Springer, 2024

2024
[186]

Freestyle layout-to-image synthesis

Han Xue, Zhiwu Huang, Qianru Sun, Li Song, and Wenjun Zhang. Freestyle layout-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14256–14266, 2023

2023
[187]

Gpt-imgeval: A comprehensive benchmark for diagnosing gpt4o in image generation

Zhiyuan Yan, Junyan Ye, Weijia Li, Zilong Huang, Shenghai Yuan, Xiangyang He, Kaiqing Lin, Jun He, Conghui He, and Li Yuan. Gpt-imgeval: A comprehensive benchmark for diagnosing gpt4o in image generation. arXiv preprint arXiv:2504.02782, 2025

2025 arXiv
[188]

Deep learning technique for human parsing: A survey and outlook

Lu Yang, Wenhe Jia, Shan Li, and Qing Song. Deep learning technique for human parsing: A survey and outlook. International Journal of Computer Vision, 132(8):3270–3301, 2024

2024
[189]

Renovating parsing r-cnn for accurate multiple human parsing

Lu Yang, Qing Song, Zhihui Wang, Mengjie Hu, Chun Liu, Xueshi Xin, Wenhe Jia, and Songcen Xu. Renovating parsing r-cnn for accurate multiple human parsing. In European Conference on computer vision, pages 421–437. Springer, 2020

2020
[190]

Parsing r-cnn for instance-level hu- man analysis

Lu Yang, Qing Song, Zhihui Wang, and Ming Jiang. Parsing r-cnn for instance-level hu- man analysis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 364–373, 2019

2019
[191]

Quality-aware network for human parsing

Lu Yang, Qing Song, Zhihui Wang, Zhiwei Liu, Songcen Xu, and Zhihao Li. Quality-aware network for human parsing. IEEE Transactions on Multimedia, 25:7128–7138, 2022

2022
[192]

3d human pose estimation in the wild by adversarial learning

Wei Yang, Wanli Ouyang, Xiaolong Wang, Jimmy Ren, Hongsheng Li, and Xiaogang Wang. 3d human pose estimation in the wild by adversarial learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5255–5264, 2018

2018
[193]

Deep joint rain detection and removal from a single image

Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1357–1366, 2017. 218

2017
[194]

The dawn of lmms: Preliminary explorations with gpt-4v (ision)

Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. The dawn of lmms: Preliminary explorations with gpt-4v (ision). arXiv preprint arXiv:2309.17421, 9(1):1, 2023

2023 arXiv
[195]

Cascaded diffusion models for 2d and 3d microscopy image synthesis to enhance cell segmentation

Rüveyda Yilmaz, Kaan Keven, Yuli Wu, and Johannes Stegmaier. Cascaded diffusion models for 2d and 3d microscopy image synthesis to enhance cell segmentation. arXiv preprint arXiv:2411.11515, 2024

2024 arXiv
[196]

Long-term photometric consistent novel view synthesis with diffusion models

Jason J Yu, Fereshteh Forghani, Konstantinos G Derpanis, and Marcus A Brubaker. Long-term photometric consistent novel view synthesis with diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7094–7104, 2023

2023
[197]

Scaling autoregressive models for content-rich text-to-image generation

Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Han Zhang, Jason Baldridge, and Yonghui Wu. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2022

2022 arXiv
[198]

Grayscale image colorization methods: Overview and evaluation

Ivana Žeger, Sonja Grgic, Josip Vukovi´c, and Gordan Šišul. Grayscale image colorization methods: Overview and evaluation. IEEE access, 9:113326–113346, 2021

2021
[199]

Optical flow and scene flow estimation: A survey

Mingliang Zhai, Xuezhi Xiang, Ning Lv, and Xiangdong Kong. Optical flow and scene flow estimation: A survey. Pattern Recognition, 114:107861, 2021

2021
[200]

Image de-raining using a conditional generative adversarial network

He Zhang, Vishwanath Sindagi, and Vishal M Patel. Image de-raining using a conditional generative adversarial network. IEEE transactions on circuits and systems for video technology, 30(11):3943–3956, 2019

2019
[201]

Magicbrush: A manually annotated dataset for instruction-guided image editing

Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. Magicbrush: A manually annotated dataset for instruction-guided image editing. Advances in Neural Information Processing Systems, 36:31428–31449, 2023

2023
[202]

Adding conditional control to text-to-image diffusion models

Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023

2023
[203]

Colorful image colorization

Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, pages 649–666. Springer, 2016

2016
[204]

Omg-llava: Bridging image-level, object-level, pixel-level reasoning and understanding

Tao Zhang, Xiangtai Li, Hao Fei, Haobo Yuan, Shengqiong Wu, Shunping Ji, Chen Change Loy, and Shuicheng Yan. Omg-llava: Bridging image-level, object-level, pixel-level reasoning and understanding. Advances in Neural Information Processing Systems, 37:71737–71767, 2024

2024
[205]

Underwater image enhancement via weighted wavelet visual perception fusion

Weidong Zhang, Ling Zhou, Peixian Zhuang, Guohou Li, Xipeng Pan, Wenyi Zhao, and Chongyi Li. Underwater image enhancement via weighted wavelet visual perception fusion. IEEE Transactions on Circuits and Systems for Video Technology, 34(4):2469–2483, 2023

2023
[206]

Itercomp: Iterative composition-aware feedback learning from model gallery for text-to-image generation

Xinchen Zhang, Ling Yang, Guohao Li, Yaqi Cai, Jiake Xie, Yong Tang, Yujiu Yang, Mengdi Wang, and Bin Cui. Itercomp: Iterative composition-aware feedback learning from model gallery for text-to-image generation. arXiv preprint arXiv:2410.07171, 2024

arXiv
[207]

Texcontrol: Sketch-based two-stage fashion image generation using diffusion model

Yongming Zhang, Tianyu Zhang, and Haoran Xie. Texcontrol: Sketch-based two-stage fashion image generation using diffusion model. In 2024 Nicograph International (NicoInt), pages 64–68. IEEE, 2024

2024
[208]

Prospect: Prompt spectrum for attribute-aware personalization of diffusion models

Yuxin Zhang, Weiming Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Tong- Yee Lee, Oliver Deussen, and Changsheng Xu. Prospect: Prompt spectrum for attribute-aware personalization of diffusion models. ACM Transactions on Graphics (TOG), 42(6):1–14, 2023

2023
[209]

Udifftext: A unified framework for high-quality text synthesis in arbitrary images via character-aware diffusion models

Yiming Zhao and Zhouhui Lian. Udifftext: A unified framework for high-quality text synthesis in arbitrary images via character-aware diffusion models. arXiv preprint arXiv:2312.04884, 2023. 219

2023 arXiv
[210]

Layout- diffusion: Controllable diffusion model for layout-to-image generation

Guangcong Zheng, Xianpan Zhou, Xuewei Li, Zhongang Qi, Ying Shan, and Xi Li. Layout- diffusion: Controllable diffusion model for layout-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22490–22499, 2023

2023
[211]

Curricular contrastive regularization for physics-aware single image dehazing

Yu Zheng, Jiahui Zhan, Shengfeng He, Junyu Dong, and Yong Du. Curricular contrastive regularization for physics-aware single image dehazing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5785–5794, 2023

2023
[212]

Blur interpolation transformer for real-world motion from blur

Zhihang Zhong, Mingdeng Cao, Xiang Ji, Yinqiang Zheng, and Imari Sato. Blur interpolation transformer for real-world motion from blur. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5713–5723, 2023

2023
[213]

Scene parsing through ade20k dataset

Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 633–641, 2017

2017
[214]

Semantic understanding of scenes through the ade20k dataset

Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127:302–321, 2019

2019
[215]

Lifting by image–leveraging image cues for accurate 3d human pose estimation

Feng Zhou, Jianqin Yin, and Peiyang Li. Lifting by image–leveraging image cues for accurate 3d human pose estimation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 7632–7640, 2024

2024
[216]

Deep single-image portrait relighting

Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, and David W Jacobs. Deep single-image portrait relighting. In Proceedings of the IEEE/CVF international conference on computer vision , pages 7194–7202, 2019

2019
[217]

Storydiffusion: Consistent self-attention for long-range image and video generation

Yupeng Zhou, Daquan Zhou, Ming-Ming Cheng, Jiashi Feng, and Qibin Hou. Storydiffusion: Consistent self-attention for long-range image and video generation. Advances in Neural Information Processing Systems, 37:110315–110340, 2024

2024
[218]

Tryondiffusion: A tale of two unets

Luyang Zhu, Dawei Yang, Tyler Zhu, Fitsum Reda, William Chan, Chitwan Saharia, Moham- mad Norouzi, and Ira Kemelmacher-Shlizerman. Tryondiffusion: A tale of two unets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4606–4615, 2023

2023
[219]

Designing an illumination-aware network for deep image relighting

Zuo-Liang Zhu, Zhen Li, Rui-Xun Zhang, Chun-Le Guo, and Ming-Ming Cheng. Designing an illumination-aware network for deep image relighting. IEEE Transactions on Image Processing, 31:5396–5411, 2022. 220

2022

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Defocus deblurring using dual-pixel data

Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16, pages 111–126. Springer, 2020

2020

[2] [2]

Learning to reduce defocus blur by realistically modeling dual-pixel data

Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S Brown, and Peyman Milanfar. Learning to reduce defocus blur by realistically modeling dual-pixel data. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 2289– 2298, 2021

2021

[3] [3]

Ntire 2017 challenge on single image super-resolution: Dataset and study

Eirikur Agustsson and Radu Timofte. Ntire 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 126–135, 2017

2017

[4] [4]

Dream360: Diverse and immersive outdoor virtual scene creation via transformer-based 360 image outpainting

Hao Ai, Zidong Cao, Haonan Lu, Chen Chen, Jian Ma, Pengyuan Zhou, Tae-Kyun Kim, Pan Hui, and Lin Wang. Dream360: Diverse and immersive outdoor virtual scene creation via transformer-based 360 image outpainting. IEEE transactions on visualization and computer graphics, 2024

2024

[5] [5]

Single-image reflection removal using deep learning: a systematic review

Ali Amanlou, Amir Abolfazl Suratgar, Jafar Tavoosi, Ardashir Mohammadzadeh, and Amir Mosavi. Single-image reflection removal using deep learning: a systematic review. IEEE Access, 10:29937–29953, 2022

2022

[6] [6]

2d human pose esti- mation: New benchmark and state of the art analysis

Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, and Bernt Schiele. 2d human pose esti- mation: New benchmark and state of the art analysis. In Proceedings of the IEEE Conference on computer Vision and Pattern Recognition, pages 3686–3693, 2014

2014

[7] [7]

Change detection techniques for remote sensing applications: A survey

Anju Asokan and JJESI Anitha. Change detection techniques for remote sensing applications: A survey. Earth Science Informatics, 12:143–160, 2019

2019

[8] [8]

Rethinking inductive biases for surface normal estimation

Gwangbin Bae and Andrew J Davison. Rethinking inductive biases for surface normal estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9535–9545, 2024

2024