Pith. sign in

REVIEW 3 major objections 6 minor 1 cited by

Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding

T0 review · 3 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 Explicit arrow-direction encoding raises VLM accuracy on flowchart question answering from 80% to 89% and to 100% on next-step queries.

desk verdict A reasonable system paper with a real but tiny measurement whose headline attribution to arrow-direction encoding is not isolated by the experiment; fixable with one ablation, but as written the claim outruns the data. read the letter →

arxiv 2505.07864 v1 pith:WGZ22FCJ submitted 2025-05-09 cs.AI cs.CLcs.CV

Takamitsu Omasa , Ryo Koshihara , Masumi Morishige This is my paper · ORCID
classification cs.AIcs.CLcs.CV
keywords flowchartunderstandingvision-languagemodelsarrowdirectionencodinggraph-structuredpromptingobjectdetectionOCRdiagramquestionansweringgraphtopology
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

This paper claims that a vision-language model can answer questions about flowcharts far more reliably when the diagram is first parsed by a physical pipeline—optical character recognition plus a fine-tuned object detector that explicitly localises arrow starts and ends—and the parsed information is handed to the model as a structured, coordinate-rich text prompt rather than as pixels alone. On a 90-question benchmark built from 30 annotated flowcharts, overall accuracy rises from 80% to 89% with no task-specific fine-tuning of the language model, and 'what is the next step?' questions are answered correctly 100% of the time. The authors interpret the gains as evidence that explicit arrow-direction and graph-topology encoding closes a reasoning gap that end-to-end vision-language models leave open. The claim matters because it suggests a modular route to making process diagrams, engineering schematics, and similar structured images queryable by today's language models without retraining them.

What carries the argument

The load-bearing mechanism is the coordinate-rich graph-structured prompt: each flowchart entity is serialised as (text, category, normalised centre-of-mass) and each object's neighbours are listed as preceding and subsequent steps derived from the recovered arrow endpoints. Arrow direction is made explicit by fine-tuned detection of tiny 'Arrow Start' and 'Arrow End' boxes, which are matched to Arrow boxes by near-edge location and IoU > 0.5, and then used to compute incoming and outgoing links. The paper argues this gives the otherwise text-blind language model a geometry channel—a way to see who points at whom—so that it infers edge orientation from spatial coordinates rather than hallucinating it from an unordered token list.

What would settle it

Run the same 90-question benchmark with the full pipeline but strip only the arrow-derived information—the 'Arrow Start'/'Arrow End' coordinates and the preceding/subsequent links—while keeping OCR text and object boxes. If accuracy stays at 89%, the claim that arrow-direction encoding is what drives the gain is refuted; if it falls back toward 80%, the claim survives.

Watch

Extended reading notes

Core claim

The central discovery, stated on the paper's own terms, is that recovering the directional skeleton of a flowchart before reasoning lets a general-purpose vision-language model answer diagram questions it otherwise misses. The proposed pipeline performs OCR, detects nine classes of flowchart objects including small boxes around arrow starts and arrow ends, fuses each text box with the object it overlaps by more than 50%, links arrows to their endpoints by near-edge proximity and a 0.5 IoU criterion, links non-arrow objects to arrows, and then serialises every entity as a category, text, and normalised centre-of-mass coordinate in a graph-structured prompt that lists each object's preceding and subsequent steps. Fed this prompt together with the original image, the model reaches 80/90 (88.9%) overall versus 72/90 (80.0%) for the image-only baseline; next-step questions improve from 25/30 to 30/30, conditional-branch questions from 41/50 to 45/50, and large-diagram accuracy from 66.7% to 80.0%. Preceding-step questions remain difficult, with the baseline at 60% and the pipeline at 50%.

Load-bearing premise

The paper attributes the accuracy gain to arrow-direction encoding, but the full pipeline differs from the baseline by adding OCR text, detected object boxes, coordinate tuples, and graph structure all at once, so the reported experiments do not isolate arrows as the cause of the improvement.

Editorial extensions

If this is right

  • Next-step understanding of clean, printed flowcharts at this scale is effectively solved: 30/30 on such queries with the proposed pipeline.
  • Diagram QA can be improved modularly—by improving the detector or OCR, not the language model—so better small-object detection of arrowheads should translate directly into higher task accuracy.
  • The pipeline transfers the burden of visual parsing out of the VLM, so the same reasoning model can be pointed at new diagram types (e.g., BPMN or UML) by retraining only the detector and OCR fusion, not the language model.
  • On large, arrow-dense diagrams the gain is largest (+13.3 pp), suggesting geometry-aware prompts matter most exactly where end-to-end VLMs fail.

Reading between the lines

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

  • Because the comparison adds several variables at once, the most credible reading of the +9 pp is that supplying readable text plus explicit structure together helps; which component carries the weight is untested, and an OCR-without-arrows ablation would settle it.
  • On 30 next-step questions, 100% accuracy has a wide confidence interval; the true next-step performance on a larger or messier corpus is likely lower, especially for handwritten diagrams or nodes with multiple incoming edges, which the paper itself flags as a residual error source.
  • If arrow recovery is indeed the driver, then detector precision on tiny arrowheads (reported mAP 0.2349 at standard IoU) is the bottleneck, and improving that metric should yield outsized QA gains relative to prompt tweaks.
  • The near-zero LLM-judge advantage on conditional-branch questions, despite a human-judged +8 pp gain, suggests part of the measured improvement depends on paraphrase-tolerant evaluation; a strict automatic judge may underestimate genuine gains on open-ended questions.
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.

Referee Report

3 major / 6 minor

Summary. The manuscript proposes a seven-stage inference pipeline for flowchart question answering: OCR (Azure AI Document Intelligence), fine-tuned DAMO-YOLO object detection with arrow-start/arrow-end classes, text-object association, arrow-start/end linking, node-arrow linking, graph-structured prompt construction, and GPT-4o reasoning. The authors evaluate the pipeline on 90 questions derived from 30 manually annotated flowcharts, covering next-step, conditional-branch, and previous-step question types, by comparing an OCR+detection+graph-prompt condition against a raw-image GPT-4o baseline. They report an overall accuracy gain from 80.0% (72/90) to 88.9% (80/90), perfect 100% on the 30 next-step questions, per-size gains on Large and Medium diagrams, and LLM-as-judge results as a secondary check. The paper explicitly acknowledges the small test set and the dependence on OCR and detector quality.

Significance. If the central claim were established, the paper would offer a simple, modular recipe (explicit coordinates and arrow topology in the prompt) that improves VLM performance on diagram reasoning without VLM fine-tuning, plus a small annotated benchmark for flowchart QA. The paper is transparent about raw counts, reports per-category detection performance in the appendix, and states its limitations clearly. However, the current experimental design does not isolate the arrow-direction-encoding mechanism from the simultaneous addition of OCR text, bounding-box coordinates, and graph structure, and the 'statistically significant' claim is not supported by any reported statistical procedure. A direction-reversal or arrow-removal ablation, paired significance tests, and a better-documented human evaluation protocol are needed before the title-level claim can be considered established.

major comments (3)
  1. [§4, Tables 2–4] The reported +9 pp gain cannot be attributed to arrow-direction encoding, because the Ocr-Dec condition and the No-Ocr-Dec baseline differ simultaneously in OCR text, detected boxes and coordinates, arrow-start/end linking, and graph-structured prompting. No condition removes only the arrow information; a condition with the same OCR and detection outputs but with arrow edges shuffled, reversed, or omitted is needed. The per-type numbers worsen this concern: Type 3 actually degrades (5/10 vs. 6/10), and the aggregate improvement rests on a handful of Type 1 and Type 2 responses. Please add at least one ablation that isolates arrow direction and discuss the per-type results in that light.
  2. [§4.2–4.3, abstract] The abstract calls the +9 pp gain 'statistically significant,' but no statistical test, confidence interval, or paired analysis is reported. For independent proportions, 80/90 vs. 72/90 gives p approximately 0.10, and the paired structure (the same 30 diagrams are used in both conditions) requires McNemar's test, for which the discordant counts are not given. The Type 3 cell has only 10 questions and the Type 2 cell has 50; the per-type deltas are +5, +4, and -1. Please report exact paired tests and confidence intervals, or remove the significance claim and describe the improvement as an observed gain on a small sample.
  3. [§4.2] The primary human-evaluation protocol is described in only one sentence ('correctness was determined by comparing the predicted objectB in the flowchart with the ground-truth object described as A is B'), with no information about the number of annotators, whether they were blinded to condition, the instructions they received, or inter-annotator agreement. Because the LLM-as-judge check uses GPT-4o, the same model family as the system under test, it cannot serve as an independent validation, so the human numbers carry the entire weight of the comparison. Please document the human evaluation protocol in full and report agreement statistics.
minor comments (6)
  1. [§3.2–3.3] The Text class is listed among the nine annotated classes, but §3.3 says the final implementation relies exclusively on the OCR service for text information; please clarify whether Text detections are used at all and why the class is retained in the annotation scheme.
  2. [§3.3–3.4] The 50% text-object overlap threshold and the IoU > 0.5 arrow-association threshold are hand-set and not ablated; please add a sensitivity analysis or justify the values empirically, since the reconstructed graph structure depends directly on these thresholds.
  3. [Table 1 and Table 3] Table 1 states that each diagram is paired with three types of questions, but Table 3 shows per-type denominators of 30, 50, and 10 for Types 1, 2, and 3; please explain how the 90-question total is allocated across question types and diagrams.
  4. [§4.4] The discussion attributes the discrepancy between human and LLM scores on Type 2 to the LLM judge penalizing paraphrases and extended explanations, but no examples are provided; please include representative outputs to support this claim.
  5. [§3.7, Appendix B] The exact graph-structured prompt template is not shown; Figure 1 gives an overview, but the precise serialization of (text, x, y, incoming, outgoing) tuples is essential for reproducibility and should be included in an appendix.
  6. [References and data availability] The annotated benchmark and the code are not released, and some references have incomplete metadata; since the paper introduces a benchmark, releasing the data and annotations would substantially strengthen reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central accuracy result is anchored to external human-annotated ground truth, and no prediction reduces to a fitted input or to a self-citation.

full rationale

The paper's central empirical claim is an accuracy comparison on a 90-question benchmark with human-annotated ground truth. The proposed Ocr-Dec pipeline is evaluated against a raw-image baseline; neither condition's outcome is constructed from the other or from the metric. The pipeline's parameters (OCR, DAMO-YOLO, IoU assignment thresholds, prompt serialization) are fixed design choices, not fitted to the 90 test answers, and no quantity that is later 'predicted' appears as a fitted input in an earlier stage. The paper contains no load-bearing self-citations: all cited works are external, and no 'uniqueness theorem' or ansatz is imported from the authors' prior work. The comparison's main weakness is attributional, not circular: Ocr-Dec differs from the baseline along multiple dimensions (OCR text, detected boxes, coordinates, graph-structured prompt), so the +9 pp cannot be uniquely assigned to arrow-direction encoding; and the LLM-as-a-Judge side-check uses GPT-4o to grade GPT-4o outputs, making it a non-independent supplementary signal. These are experimental-validity concerns, not reductions of a prediction to its inputs. Because the primary evaluation is external and human-judged, the derivation chain is not circular.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The pipeline relies on five unverified domain assumptions: OCR accuracy, detector generalization, VLM spatial reasoning, unbiased human evaluation, and the fidelity of heuristic arrow matching. The two hand-set thresholds, IoU above 0.5 and overlap above 50%, are design choices that affect the recovered graph but are not reported as tuned or ablated.

free parameters (3)
  • Arrow-Start/End association IoU threshold = 0.5
    Section 3.4; only arrows whose endpoint bounding boxes exceed IoU 0.5 with the arrow box are linked, and this setting directly controls the recovered direction graph.
  • Text-object overlap threshold = 50%
    Section 3.3; OCR text is assigned to a detected object when bounding-box overlap exceeds 50%, and changes would reassign node labels.
  • Diagram size cutoffs = 13 and 22 arrows
    Table 1; small, medium, and large bins are arbitrary and used for subgroup accuracy with no uncertainty reported.
assumptions (5)
  • domain assumption GPT-4o can recover edge orientations from normalized center-of-mass coordinates using geometric priors learned during pre-training.
    Section 1; the whole design assumes coordinate-rich prompts are useful for topology reasoning.
  • domain assumption Azure AI Document Intelligence OCR is accurate enough on printed flowchart text that OCR errors do not dominate the QA result.
    Section 3.1; no OCR error rate is reported on this corpus.
  • domain assumption A DAMO-YOLO detector fine-tuned on 69 annotated flowcharts generalizes to the 30 test flowcharts.
    Section 3.2; only 69 training diagrams are used and no cross-validation is reported.
  • domain assumption Human judgment is a reliable ground-truth label without significant annotator disagreement or bias.
    Section 4.2; no inter-annotator agreement is reported and evaluators are not shown to be blind to condition.
  • domain assumption Arrow endpoints that satisfy the IoU and edge-proximity heuristics represent the true source and target of each edge.
    Sections 3.4 and 3.5; the heuristic graph construction is not validated against ground-truth edge labels.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding." pith.science (2026). https://pith.science/paper/WGZ22FCJ

@misc{pith2026250507864,
  author       = {Pith},
  title        = {Pith review of: Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WGZ22FCJ}},
  note         = {Machine review of arXiv:2505.07864}
}
read the original abstract

Flowcharts are indispensable tools in software design and business-process analysis, yet current vision-language models (VLMs) frequently misinterpret the directional arrows and graph topology that set these diagrams apart from natural images. We introduce a seven-stage pipeline grouped into three broader processes: (1) arrow-aware detection of nodes and arrow endpoints; (2) optical character recognition (OCR) to extract node text; and (3) construction of a structured prompt that guides the VLMs. Tested on a 90-question benchmark distilled from 30 annotated flowcharts, the method raises overall accuracy from 80 % to 89 % (+9 percentage points) without any task-specific fine-tuning. The gain is most pronounced for next-step queries (25/30 -> 30/30; 100 %, +17 pp); branch-result questions improve more modestly, and before-step questions remain difficult. A parallel evaluation with an LLM-as-a-Judge protocol shows the same trends, reinforcing the advantage of explicit arrow encoding. Limitations include dependence on detector and OCR precision, the small evaluation set, and residual errors at nodes with multiple incoming edges. Future work will enlarge the benchmark with synthetic and handwritten flowcharts and assess the approach on Business Process Model and Notation (BPMN) and Unified Modeling Language (UML).

Figures

Figures reproduced from arXiv: 2505.07864 by the authors.

Figure 1
Figure 1. Overview of the seven-stage pipeline: OCR, object detection, text–object fusion, arrow [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

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

  1. Survey of GenAI for Automotive Software Development: From Requirements to Executable Code

    cs.SE 2025-07 conditional novelty 3.0 of 10

    A review of roughly 60 papers and 9 industry respondents finds GPT-family models dominate automotive code generation while requirements handling lags due to confidentiality constraints.

Reference graph

Works this paper leans on

28 extracted references · 14 canonical work pages · cited by 1 Pith paper

  1. [1]

    Hooshyar, R.B

    D. Hooshyar, R.B. Ahmad, M. Yousefi, F.D. Yusop, and S.-J. Horng. A flowchart-based intelligent tutoring system for improving problem-solving skills of novice programmers. Journal of Computer Assisted Learning, 31(4):345–361, apr 2015. ISSN 1365-2729. doi: 10.1111/jcal.12099. URL http://dx.doi.org/10.1111/jcal.12099

    work page doi:10.1111/jcal.12099 2015

  2. [2]

    Towards makingflowchartimagesmachineinterpretable

    Shreya Shukla, Prajwal Gatti, Yogesh Kumar, Vikash Yadav, and Anand Mishra. Towards makingflowchartimagesmachineinterpretable. 2025. URL http://arxiv.org/pdf/2501. 17441

    work page 2025

  3. [3]

    Measuring massive multitask language understanding

    Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. 2020. URLhttp: //arxiv.org/pdf/2009.03300

    arXiv 2020

  4. [4]

    Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, and OTHERS

    OpenAI, :, Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, and OTHERS. Gpt-4o system card. 2024. URL http://arxiv.org/pdf/2410.21276

    arXiv 2024

  5. [5]

    https://openai.com/index/hello-gpt-4o/, 2025

    Hello gpt-4o | openai. https://openai.com/index/hello-gpt-4o/, 2025. Accessed: 2025-04-30

    work page 2025

  6. [6]

    Mmmu: Amassivemulti-discipline multimodal understanding and reasoning benchmark for expert agi

    Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, DongfuJiang,WeimingRen,YuxuanSun,andOTHERS. Mmmu: Amassivemulti-discipline multimodal understanding and reasoning benchmark for expert agi. 2023. URLhttp:// arxiv.org/pdf/2311.16502

    arXiv 2023

  7. [7]

    Mathvista: Evaluatingmathematical reasoning of foundation models in visual contexts

    Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng,Kai-WeiChang,MichelGalley,andJianfengGao. Mathvista: Evaluatingmathematical reasoning of foundation models in visual contexts. 2023. URLhttp://arxiv.org/pdf/ 2310.02255

    arXiv 2023

  8. [8]

    Minesh Mathew, Dimosthenis Karatzas, and C. V. Jawahar. Docvqa: A dataset for vqa on document images. 2020. URLhttp://arxiv.org/pdf/2007.00398

    arXiv 2020

Show all 28 references
  1. [9]

    FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding

    HuitongPan,QiZhang,CorneliaCaragea,EduardDragut,andLonginJanLatecki. FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding. IOS Press, oct 2024. ISBN 9781643685489. doi: 10.3233/faia240473. URL http://dx.doi.org/10.3233/ FAIA240473

    2024 doi

  2. [10]

    Youonlylookonce: Unified, real-time object detection

    JosephRedmon,SantoshDivvala,RossGirshick,andAliFarhadi. Youonlylookonce: Unified, real-time object detection. 2015. URLhttp://arxiv.org/pdf/1506.02640. 8

    2015 arXiv

  3. [11]

    Berg, Wan-Yen Lo, and OTHERS

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, and OTHERS. Segment anything. 2023. URLhttp://arxiv.org/pdf/2304.02643

    2023 arXiv

  4. [12]

    GenFlowchart: Parsing and Understanding Flowchart Using Generative AI, page 99–111

    AbdulArbaz,HengFan,JunhuaDing,MeikangQiu,andYunheFeng. GenFlowchart: Parsing and Understanding Flowchart Using Generative AI, page 99–111. Springer Nature Singapore,

  5. [13]

    Arrow r-cnn for handwritten diagram recognition

    Bernhard Schäfer, Margret Keuper, and Heiner Stuckenschmidt. Arrow r-cnn for handwritten diagram recognition. International Journal on Document Analysis and Recognition (IJDAR), 24(1–2):3–17, feb 2021. ISSN 1433-2825. doi: 10.1007/s10032-020-00361-1. URLhttp: //dx.doi.org/10.10...

    2021 doi

  6. [14]

    Are we on the right way for evaluating large vision-language models? 2024

    Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, and OTHERS. Are we on the right way for evaluating large vision-language models? 2024. URLhttp://arxiv.org/pdf/2403.20330

    2024 arXiv

  7. [15]

    A diagram is worth a dozen images

    Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. A diagram is worth a dozen images. 2016. URLhttp://arxiv.org/pdf/1603. 07396

    2016

  8. [16]

    Chartinsights: Evaluating multimodal large language models for low-level chart question answering

    Yifan Wu, Lutao Yan, Leixian Shen, Yunhai Wang, Nan Tang, and Yuyu Luo. Chartinsights: Evaluating multimodal large language models for low-level chart question answering. 2024. URL http://arxiv.org/pdf/2405.07001

    2024 arXiv

  9. [17]

    Charxiv: Charting gaps in realistic chart understanding in multimodal llms

    Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, and OTHERS. Charxiv: Charting gaps in realistic chart understanding in multimodal llms. 2024. URLhttp://arxiv.org/pdf/2406.18521

    2024 arXiv

  10. [18]

    Chartx & chartvlm: A versatile benchmark andfoundationmodelforcomplicatedchartreasoning

    Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Peng Ye, Min Dou, Botian Shi, and OTHERS. Chartx & chartvlm: A versatile benchmark andfoundationmodelforcomplicatedchartreasoning. 2024. URL http://arxiv.org/pdf/ 2402.12185

    2024 arXiv

  11. [19]

    Shikra: Unleashing multimodal llm’s referential dialogue magic

    Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: Unleashing multimodal llm’s referential dialogue magic. 2023. URLhttp://arxiv.org/ pdf/2306.15195

    2023 arXiv

  12. [20]

    Chatspot: Bootstrapping multimodal llms via precise referring instruction tuning

    LiangZhao,EnYu,ZhengGe,JinrongYang,HaoranWei,HongyuZhou,JianjianSun,Yuang Peng, Runpei Dong, Chunrui Han, and OTHERS. Chatspot: Bootstrapping multimodal llms via precise referring instruction tuning. 2023. URLhttp://arxiv.org/pdf/2307.09474

    2023 arXiv

  13. [21]

    Region- blip: A unified multi-modal pre-training framework for holistic and regional comprehension

    QiangZhou,ChaohuiYu,ShaofengZhang,SitongWu,ZhibingWang,andFanWang. Region- blip: A unified multi-modal pre-training framework for holistic and regional comprehension

  14. [22]

    Grcnn: Graph recognition convolutional neural network for synthesizingprogramsfromflowcharts

    Lin Cheng and Zijiang Yang. Grcnn: Graph recognition convolutional neural network for synthesizingprogramsfromflowcharts. 2020. URL http://arxiv.org/pdf/2011.05980

    2020 arXiv

  15. [23]

    Damo-yolo : A report on real-time object detection design

    XianzheXu,YiqiJiang,WeihuaChen,YilunHuang,YuanZhang,andXiuyuSun. Damo-yolo : A report on real-time object detection design. 2022. URLhttp://arxiv.org/pdf/2211. 15444. A Additional Evaluation Results We provide here additional results and analysis that complement the main paper...

    2022

  16. [26]

    A question (Question)

  17. [27]

    A reference answer (Reference Answer)

  18. [28]

    - Determine whether they essentially match in meaning or reasoning, or if the model output is otherwise correct based on its logic and evidence

    A model output (Model Output) Please evaluate the model output by following these steps: ### Step 1: Analyze the Answers - First, compare the reference answer and the model output. - Determine whether they essentially match in meaning or reasoning, or if the model output is ot...

  19. [2023]

    URL http://arxiv.org/pdf/2308.02299

    arXiv

  20. [2024]

    doi: 10.1007/978-981-97-5492-2_8

    ISBN 9789819754922. doi: 10.1007/978-981-97-5492-2_8. URL http://dx.doi. org/10.1007/978-981-97-5492-2_8

    doi

Pith tools

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