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REVIEW 5 major objections 5 minor 4 cited by

Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models

T0 review · 5 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read Anomaly-OV uses a Look-Twice Feature Matching mechanism to make a multimodal LLM detect and explain fine-grained anomalies in classes never seen during training.

desk verdict A genuinely useful dataset and benchmark wrapped around a plausible specialist MLLM, but the zero-shot protocol is ambiguous enough that the headline claim shouldn't be taken on faith. read the letter →

arxiv 2502.07601 v2 pith:UPRSBQCT submitted 2025-02-11 cs.CV cs.CL

Jiacong Xu , Shao-Yuan Lo , Bardia Safaei , Vishal M. Patel , Isht Dwivedi This is my paper · ORCID
classification cs.CVcs.CL
keywords zero-shotanomalydetectionmultimodallargelanguagemodelsreasoningvisualinstructiontuningLook-TwiceFeatureMatchinglocalizationVisA-D&RAnomaly-Instruct-125k
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 sets out to show that zero-shot anomaly detection and reasoning can be combined in one visual assistant, and that the reason current multimodal LLMs fail at this task is that they cannot pick out the visual tokens that carry the defect. To that end it builds Anomaly-Instruct-125k, a 125k-sample visual instruction-tuning dataset covering industrial, medical, in-the-wild, and 3D images, and VisA-D&R, a human-reviewed benchmark of 1,761 industrial images for detection and reasoning. On that benchmark, Anomaly-OV-7B reaches 0.79 accuracy and 0.83 F1 for text-based anomaly detection, compared with GPT-4o's 0.70 and 0.68, and it scores higher on both low-level and complex reasoning metrics. Its anomaly expert alone reaches an average AUROC of 88.6 across nine zero-shot benchmarks. If the claim holds, inspection systems would no longer need per-class normal training data and could explain the cause and impact of each defect in plain language.

What carries the argument

The load-bearing mechanism is Look-Twice Feature Matching (LTFM), a two-pass attention steered by an anomaly expert. In the look-back pass, the global feature $\mathbf{v}^o_0$ of the original image is combined with two learnable embeddings, $e^+$ for anomalous patterns and $e^-$ for normal patterns, through level-specific linear layers and MLPs, producing anomaly and normality descriptions $d^+_i$ and $d^-_i$ for each of four ViT levels. In the forward pass, each patch feature $\mathbf{v}^i_j$ is compared with both descriptions by cosine similarity and softmax to form a significance map $m^i_j$, and the four maps are averaged into $m_j$. The Visual Token Selector multiplies patch tokens by $m_j$, average-pools them to a $2\times2$ grid, and uses a Q-Former with the original tokens as keys and values to output a compact set of emphasized tokens, plus a global anomaly vector $r(I)$ that yields an image-level score through a sigmoid MLP. These emphasized tokens are inserted into the LLM with an '<adv> suspicious feature:' prompt so the model attends to the selected regions. The mechanism embodies the paper's key claim: generalist MLLMs already encode anomaly information in their visual tokens but need an external expert to point at the right tokens.

What would settle it

Hold all VisA classes and all medical benchmarks out of Anomaly-Instruct-125k, train Anomaly-OV on the remaining data, and compare its significance maps with ground-truth anomaly masks on the held-out classes; if localization AUROC is near chance or text-based detection F1 drops to the level of GPT-4o, the claim that LTFM transfers zero-shot is falsified.

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Extended reading notes

Core claim

The central claim is that Anomaly-OneVision (Anomaly-OV), a specialist visual assistant built on LLaVA-OneVision, outperforms generalist multimodal LLMs at both zero-shot anomaly detection and anomaly reasoning. Its Look-Twice Feature Matching mechanism derives class-aware anomaly and normality descriptions from the image's own global feature vector plus two learnable prototypes $e^+$ and $e^-$, then scores every patch token at four ViT levels by cosine similarity to those descriptions to build significance maps. The Visual Token Selector emphasizes the suspicious tokens, spatially pools them, and aggregates them with the original tokens through a Q-Former; the selected tokens are presented to the LLM with an indication prompt whose strength depends on the predicted anomaly score. The authors report that this design lets Anomaly-OV find defects that GPT-4o and LLaVA-OneVision miss, and that it achieves the best zero-shot AUROC among compared methods while also improving low-level and complex reasoning on VisA-D&R.

Load-bearing premise

The method depends on the assumption that cosine similarity between patch features and two learned anomaly/normal description vectors reliably marks the true defect in classes and domains never seen during training; if that similarity signal misleads, the significance maps point the language model at irrelevant regions and both detection and reasoning break down.

Editorial extensions

If this is right

  • Zero-shot anomaly detection works without a text encoder: the image's own global features plus two learned prototypes supply the anomaly and normality descriptions.
  • The dataset is reusable: fine-tuning LLaVA-OneVision-0.5B on Anomaly-Instruct-125k raises text-based detection accuracy from 0.54 to 0.71 and F1 from 0.28 to 0.76.
  • The anomaly expert transfers across industrial and medical domains, averaging 88.6 AUROC on nine zero-shot benchmarks with no per-class training.
  • A specialist token-selection module can be added to an existing MLLM without large-scale pretraining, and it preserves the base model's ability to follow ordinary image instructions.
  • The same architecture extends to multi-view 3D and medical image reasoning, pointing toward a single assistant for visual inspection.

Reading between the lines

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

  • The paper's reported failure cases suggest the remaining bottleneck is the anomaly expert rather than the LLM, so a stronger expert could yield further gains without retraining the language model.
  • Because GPT-4o detects tiny defects when given bounding-box hints, the same token-selection idea may transfer to other fine-grained localization tasks, such as medical imaging or document inspection, where generalist MLLMs underuse low-level visual tokens.
  • The WebAD automatic collection pipeline implies that large in-the-wild web corpora can substitute for scarce industrial anomaly data; a testable extension is scaling WebAD and checking whether zero-shot AUROC keeps improving.
  • Passing multi-level ViT features directly into the LLM, which the paper lists as future work, is a natural next step because anomaly cues are often low-level and current MLLMs receive only the last encoder layer.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper introduces Anomaly-Instruct-125k, a visual instruction tuning dataset for anomaly detection and reasoning, and VisA-D&R, a benchmark with human-reviewed labels for evaluating both detection and reasoning. It then proposes Anomaly-OV, which augments LLaVA-OneVision with an 'anomaly expert' trained in two stages: the expert uses Look-Twice Feature Matching (LTFM) to compute per-token anomaly significance maps and a Visual Token Selector to emphasize suspicious tokens for the LLM. The central empirical claims are that the anomaly expert reaches an average AUROC of 88.6 across nine zero-shot anomaly detection benchmarks (Table 2) and that Anomaly-OV outperforms generalist MLLMs such as GPT-4o on text-based detection and reasoning on VisA-D&R (Table 4). The paper also provides ablations, qualitative comparisons, and extensions to 3D and medical domains.

Significance. If the claims hold, this is a valuable contribution: it provides the first large-scale instruction dataset and benchmark specifically for anomaly detection with reasoning, and it demonstrates a modular design in which a lightweight expert guides an MLLM's attention to anomalous tokens. The human-reviewed benchmark and the explicit failure-case analysis are strengths. The central ideas are plausible, and the qualitative examples show a clear qualitative gap in fine-grained anomaly description between Anomaly-OV and both GPT-4o and LLaVA-OneVision. However, the zero-shot protocol and the reasoning evaluation need to be tightened before the headline claims can be fully accepted.

major comments (5)
  1. [5.1, Table 2] The zero-shot protocol is underspecified at the point where the paper's headline claim is made. Stage 1 'utilizes all of the data with anomaly labels in Anomaly-Instruct-125k,' and Section 4.1 lists MVTec AD and the BMAD training set as components of Anomaly-Instruct-125k. The following sentence—'when evaluating the model on the datasets contained in the training set, the corresponding datasets are replaced by VisA'—does not say whether VisA is used as a training substitute (making the VisA row in Table 2 non-zero-shot) or as an evaluation substitute (making the MVTec row require a separately trained expert). Under either reading, the 88.6 average in Table 2 is not a single zero-shot model. Please specify the exact training/evaluation split for each of the nine rows and report the average over rows that are genuinely unseen in training.
  2. [4.1, 5.3] The reasoning evaluation has a circularity risk that should be addressed explicitly. The instruction data in Anomaly-Instruct-125k are generated by prompting GPT-4o with bounding-box hints (Section 4.1 and A1.1), and the primary reasoning metric, GPT-Score, uses GPT-4 as a judge (Section 4.2). A model fine-tuned to imitate GPT-4o's annotation style is therefore rewarded for stylistic similarity rather than for perceptual correctness. The non-GPT metrics (ROUGE-L, SBERT) are less affected and should be reported as the primary evidence for the reasoning claim, along with a blind human evaluation on a random subset or a judge from a different model family.
  3. [5.1, Tables 2 and 4] No variance or significance information is reported for any experimental result. Several headline gaps are small (e.g., Table 4 F1: 0.83 for Anomaly-OV-7B vs 0.76 for InternVL-2-8B and 0.75 for Qwen2-VL-7B), and Table 2 reports single AUROC values. Please report mean and standard deviation over at least three training/evaluation runs (or, for frozen commercial models, over multiple prompt/decoding seeds), and state the number of test images per benchmark so that the reader can judge whether the differences are meaningful.
  4. [A2, Table 6] The medical extension is at risk of training/test contamination. Anomaly-Instruct-125k includes the training set of BMAD (Section 4.1), and Stage 2 additionally uses LLaVA-Med data (A2). Table 6 evaluates Anomaly-OV on the BMAD testing set; the paper does not state whether any BMAD test images, or images of the same anatomy, were seen during instruction tuning. Please clarify the exact overlap and, if there is any, retrain without the BMAD training set or report the extension as a within-distribution fine-tuned result rather than evidence of zero-shot generalization.
  5. [5.3, Table 4] The abstract and Section 5.3 describe Anomaly-OV as achieving 'significant improvements over advanced generalist models.' In Table 4, however, Anomaly-OV is fine-tuned on 125k anomaly instructions plus an additional 350k samples from LLaVA-OneVision and LLaVA-Med data, while the generalist baselines (GPT-4o, Qwen2-VL, InternVL-2, IXC-2.5) are used zero-shot. The only matched fine-tuned baseline is LLaVA-OV-0.5B*; there is no fine-tuned LLaVA-OV-7B*. The comparison therefore conflates specialist fine-tuning with the contribution of the LTFM token-selection module. Please add a matched 7B baseline fine-tuned on the same data, and rephrase the claim as improvement over zero-shot generalists.
minor comments (5)
  1. [Throughout] The model name is inconsistent: 'LLaVA' appears as 'LLaV A' in Tables 3-5 and in the text; please use a consistent spelling.
  2. [Table 2] The caption says some results are borrowed from [6,110,114]; please include the exact evaluation protocols, dataset versions, and prompt templates used in those sources so the comparison is reproducible.
  3. [Section 5.3, Table 4] Please specify the exact prompts used for text-based detection (e.g., whether the model is asked for a one-word 'Yes/No' answer) and state how partial or non-conforming answers are handled; the caption only says 'questions designed to elicit a one-word answer.'
  4. [Section 4.2] The paper states that 10 VisA classes are selected for VisA-D&R but does not describe the selection criterion or what happens to the remaining 2 classes; this affects the benchmark's generalizability and should be stated explicitly.
  5. [Supplementary, A2] The supplementary says WebAD download links will be released, but the paper does not state whether Anomaly-Instruct-125k, VisA-D&R, evaluation code, and model weights will be released; since the contribution is partly a dataset and benchmark, a release plan is needed for reproducibility.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the central detection and zero-shot claims rest on external labels and held-out datasets, with only a minor GPT-family evaluation caveat in the reasoning metric.

full rationale

The anomaly-expert detection path is trained with balanced BCE on public anomaly labels (MVTec, the BMAD training split, and other public sources) and evaluated with standard AUROC on held-out benchmarks; no benchmark score or label appears in the loss, and Equations (3)-(9) are learned functions rather than definitions of the target quantity. The Stage-1 sentence 'when evaluating the model on the datasets contained in the training set, the corresponding datasets are replaced by VisA' follows the prior cross-dataset ZSAD protocol of AdaCLIP/AnomalyCLIP, meaning the target test dataset is swapped out of training rather than being included; the text is terse and the resulting single 'Ours' row in Table 2 may blend per-target training swaps, but that is a protocol/reproducibility concern, not a circular reduction. The reasoning evaluation uses GPT-Score with GPT-4 as judge over VisA-D&R ground truth that was generated by GPT-4o with human review, and Anomaly-OV is trained on GPT-4o-generated instruction text; this creates a same-family stylistic loop that warrants caution in interpreting GPT-Score, but the same table also reports ROUGE-L and SBERT, and detection is assessed on human-examined labels. There is no load-bearing self-citation chain: the self-cited works appear only as related-work examples, and no uniqueness theorem or ansatz is imported from the authors' own prior work. Consequently the derivation is not equivalent to its inputs; any circularity is at most a minor evaluation caveat, not a reduction of the central claim.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

No new physical entities are postulated. The positive/negative prototypes and <adv> token are learned parameters within the model, not independent inventions. The main implicit assumptions are about feature separability, data-label quality, and metric validity.

free parameters (5)
  • Positive/negative prototype embeddings e+ and e- = Learned during Stage 1, values not reported
    These trainable vectors define anomaly and normality directions in Eq. 3; the significance maps in Eq. 4 depend on them.
  • Temperature tau in Eq. 4 = Not specified
    Scales the cosine similarity before softmax; chosen by hand or as a learned scalar.
  • Thresholds slow and shigh for <adv> token = Not specified
    Decide whether the indication prompt says 'highly', 'moderately', or 'slightly' based on score(I), as described in Section 3.5.
  • Spatial pooling size h=w=2 = 2x2
    Empirical trade-off choice in the Visual Token Selector (Section 3.4) to reduce token count.
  • Four selected ViT layers = Four layers, indices not listed
    Multi-level features are extracted from four ViT layers (Eq. 2); the exact layer choice is a design decision.
assumptions (4)
  • domain assumption CLIP patch features are linearly separable for anomaly versus normal local regions via cosine similarity to learned prototypes
    Eq. 4 applies softmax over cosine similarities, following CLIP zero-shot classification. This is the core inductive bias of LTFM.
  • domain assumption GPT-4o, when given bounding-box or short-text hints, produces sufficiently accurate anomaly descriptions to serve as training ground truth
    Section A1.1 motivates the dataset pipeline with examples, but Anomaly-Instruct-125k is not fully human-reviewed, so label noise is possible.
  • domain assumption The indication prompt '<adv> suspicious feature:' causes the LLM to weight the selected tokens more heavily during text generation
    Section 3.5 explains the mechanism; it is tested only through end-to-end comparisons, not isolated.
  • domain assumption ROUGE-L, SBERT, and GPT-Score are valid proxies for anomaly reasoning quality
    Section 4.2 uses these metrics; GPT-Score relies on GPT-4 as a judge, which may favor models that imitate GPT-4o output style.

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Cite this review

Pith. "Pith review of Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models." pith.science (2026). https://pith.science/paper/UPRSBQCT

@misc{pith2026250207601,
  author       = {Pith},
  title        = {Pith review of: Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UPRSBQCT}},
  note         = {Machine review of arXiv:2502.07601}
}
read the original abstract

Zero-Shot Anomaly Detection (ZSAD) is an emerging AD paradigm. Unlike the traditional unsupervised AD setting that requires a large number of normal samples to train a model, ZSAD is more practical for handling data-restricted real-world scenarios. Recently, Multimodal Large Language Models (MLLMs) have shown revolutionary reasoning capabilities in various vision tasks. However, the reasoning of image abnormalities remains underexplored due to the lack of corresponding datasets and benchmarks. To facilitate research in AD & reasoning, we establish the first visual instruction tuning dataset, Anomaly-Instruct-125k, and the evaluation benchmark, VisA-D&R. Through investigation with our benchmark, we reveal that current MLLMs like GPT-4o cannot accurately detect and describe fine-grained anomalous details in images. To address this, we propose Anomaly-OneVision (Anomaly-OV), the first specialist visual assistant for ZSAD and reasoning. Inspired by human behavior in visual inspection, Anomaly-OV leverages a Look-Twice Feature Matching (LTFM) mechanism to adaptively select and emphasize abnormal visual tokens. Extensive experiments demonstrate that Anomaly-OV achieves significant improvements over advanced generalist models in both detection and reasoning. Extensions to medical and 3D AD are provided for future study. The link to our project page: https://xujiacong.github.io/Anomaly-OV/

Figures

Figures reproduced from arXiv: 2502.07601 by the authors.

Figure 1
Figure 1. Visualization of the image-level AUROC comparison [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Industrial image anomaly reasoning results from GPT-4o [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of the Anomaly-OV architecture. It consists of two training stages: (1) professional training for the anomaly expert, and (2) visual instruction tuning for anomaly detection and reasoning. Text and visual tokens are distinguished by different colors. 3. Method 3.1. Preliminary Training an MLLM from scratch demands extensive data and computational resources to align the visual and textual embedding spaces an… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Simulation of visual anomaly inspection by humans. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Composition of the instruction data in Anomaly-Instruct-125k. There are four main types of image samples: in-the-wild, industrial, medical, and 3D (in the format of multi-view images), covering most image anomaly detection tasks and enabling the possibility of a unifie…
Figure 6
Figure 6. Figure 6: Prompt examples in VisA-D&R for detection and reason [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Visualization of the significance map on VisA samples. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Automatic data collection pipeline for WebAD. The entire pipeline is fully automatic at an affordable cost (API usage). Other [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Overview of the gallery for in-the-wild image samples in WebAD. The images on the left side are anomalous, while the right side [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Prompt template for generating multi-round conversation in Anomaly-Instruct-125k (modified from the template of LLaVA [ [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]

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Forward citations

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  3. IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning

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Reference graph

Works this paper leans on

116 extracted references · 62 canonical work pages · cited by 4 Pith papers

  1. [1]

    Bmad: Benchmarks for medical anomaly detection

    Jinan Bao, Hanshi Sun, Hanqiu Deng, Yinsheng He, Zhaoxi- ang Zhang, and Xingyu Li. Bmad: Benchmarks for medical anomaly detection. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 4042–4053, 2024. 1, 5, 6, 8, 14

    2024

  2. [2]

    Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

    Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9592–9600, 2019. 1, 5, 6, 14, 15

    2019

  3. [3]

    Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings

    Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4183–4192, 2020. 1

    2020

  4. [4]

    The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization

    Paul Bergmann, Xin Jin, David Sattlegger, and Carsten Ste- ger. The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization. In Proceedings of the 17th Inter- national Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications, 2022. 6

    2022

  5. [5]

    Anomaly de- tection under distribution shift

    Tri Cao, Jiawen Zhu, and Guansong Pang. Anomaly de- tection under distribution shift. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6511–6523, 2023. 1

    2023

  6. [6]

    Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection

    Yunkang Cao, Jiangning Zhang, Luca Frittoli, Yuqi Cheng, Weiming Shen, and Giacomo Boracchi. Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection. In European Conference on Computer Vision , pages 55–72. Springer, 2025. 1, 2, 3, 6, 7

    2025

  7. [7]

    Spatialvlm: Endow- ing vision-language models with spatial reasoning capabili- ties

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

  8. [8]

    Sharegpt4v: Improving large multi-modal models with better captions,

    Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. Sharegpt4v: Improving large multi-modal models with better captions,

Show all 116 references
  1. [9]

    A unified anomaly synthesis strategy with gradi- ent ascent for industrial anomaly detection and localization

    Qiyu Chen, Huiyuan Luo, Chengkan Lv, and Zhengtao Zhang. A unified anomaly synthesis strategy with gradi- ent ascent for industrial anomaly detection and localization. arXiv preprint arXiv:2407.09359, 2024. 1

    2024 arXiv

  2. [10]

    Clip2scene: Towards label-efficient 3d scene understanding by clip

    Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, and Wen- ping Wang. Clip2scene: Towards label-efficient 3d scene understanding by clip. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 70...

    2023

  3. [11]

    Xuhai Chen, Yue Han, and Jiangning Zhang. April-gan: A zero-/few-shot anomaly classification and segmentation method for cvpr 2023 vand workshop challenge tracks 1&2: 1st place on zero-shot ad and 4th place on few-shot ad, 2023. 2, 7

    2023

  4. [12]

    Deep one-class classification via interpolated gaussian descriptor

    Yuanhong Chen, Yu Tian, Guansong Pang, and Gustavo Carneiro. Deep one-class classification via interpolated gaussian descriptor. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 383–392, 2022. 1

    2022

  5. [13]

    How far are we to gpt-4v? closing the gap to commercial multimodal models with open- source suites, 2024

    Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhang- wei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, Ji Ma, Jiaqi Wang, Xiaoyi Dong, Hang Yan, Hewei Guo, Conghui He, Botian Shi, Zhenjiang Jin, Chao Xu, Bin Wang, Xingjian Wei, Wei Li, Wenjian Zhang, Bo Zhan...

    2024

  6. [14]

    Emotion-llama: Multimodal emotion recognition and reasoning with instruction tuning

    Zebang Cheng, Zhi-Qi Cheng, Jun-Yan He, Jingdong Sun, Kai Wang, Yuxiang Lin, Zheng Lian, Xiaojiang Peng, and Alexander Hauptmann. Emotion-llama: Multimodal emotion recognition and reasoning with instruction tuning. In Conference on Neural Information Processing Systems,

  7. [15]

    InstructBLIP: Towards general-purpose vision-language models with instruction tuning

    Wenliang Dai, Junnan Li, Dongxu Li, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. InstructBLIP: Towards general-purpose vision-language models with instruction tuning. In Thirty- seventh Conference on Neural Information Processing Sys- tems...

    2023

  8. [16]

    Automatic classification of defective photovoltaic module cells in electroluminescence images

    Sergiu Deitsch, Vincent Christlein, Stephan Berger, Claudia Buerhop-Lutz, Andreas Maier, Florian Gallwitz, and Chris- tian Riess. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, 185:455–468, 2019. 6

    2019

  9. [17]

    Simclip: Refining image-text alignment with simple prompts for zero-/few- shot anomaly detection

    Chenghao Deng, Haote Xu, Xiaolu Chen, Haodi Xu, Xiao- tong Tu, Xinghao Ding, and Yue Huang. Simclip: Refining image-text alignment with simple prompts for zero-/few- shot anomaly detection. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 1761–1770,

  10. [18]

    Anomaly detection via reverse distillation from one-class embedding

    Hanqiu Deng and Xingyu Li. Anomaly detection via reverse distillation from one-class embedding. In IEEE/CVF con- ference on computer vision and pattern recognition, 2022. 2

    2022

  11. [19]

    Anovl: Adapting vision-language models for unified zero- shot anomaly localization

    Hanqiu Deng, Zhaoxiang Zhang, Jinan Bao, and Xingyu Li. Anovl: Adapting vision-language models for unified zero- shot anomaly localization. arXiv preprint arXiv:2308.15939,

    arXiv

  12. [20]

    Bert: Pre-training of deep bidirectional trans- formers for language understanding, 2019

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional trans- formers for language understanding, 2019. 2

    2019

  13. [21]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition ...

    2021

  14. [22]

    Fastrecon: Few-shot industrial 9 anomaly detection via fast feature reconstruction

    Zheng Fang, Xiaoyang Wang, Haocheng Li, Jiejie Liu, Qi- ugui Hu, and Jimin Xiao. Fastrecon: Few-shot industrial 9 anomaly detection via fast feature reconstruction. In Pro- ceedings of the IEEE/CVF International Conference on Com- puter Vision, pages 17481–17490, 2023. 2

    2023

  15. [23]

    Chatpose: Chatting about 3d human pose

    Yao Feng, Jing Lin, Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, and Michael J Black. Chatpose: Chatting about 3d human pose. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2093– 2103, 2024. 3

    2024

  16. [24]

    Deep learning for medical anomaly detection–a survey

    Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes. Deep learning for medical anomaly detection–a survey. ACM Computing Sur- veys (CSUR), 54(7):1–37, 2021. 1

    2021

  17. [25]

    Transfusion–a transparency-based diffusion model for anomaly detection

    Matic Fu ˇcka, Vitjan Zavrtanik, and Danijel Sko ˇcaj. Transfusion–a transparency-based diffusion model for anomaly detection. In European conference on computer vision, pages 91–108. Springer, 2025. 1

    2025

  18. [26]

    Google-images-search 1.4.7, 2024

    Google. Google-images-search 1.4.7, 2024. https:// pypi.org/project/Google-Images-Search . 6, 14

    2024

  19. [27]

    Filo: Zero-shot anomaly detection by fine-grained description and high-quality lo- calization

    Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Hao Li, Ming Tang, and Jinqiao Wang. Filo: Zero-shot anomaly detection by fine-grained description and high-quality lo- calization. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 2041–2049, 2024. 1

    2024

  20. [28]

    Anomalygpt: Detecting in- dustrial anomalies using large vision-language models

    Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, and Jinqiao Wang. Anomalygpt: Detecting in- dustrial anomalies using large vision-language models. In Proceedings of the AAAI Conference on Artificial Intelli- gence, pages 1932–1940, 2024. 1, 2, 3, 5

    1932

  21. [29]

    Stimuvar: Spatiotemporal stimuli-aware video affective reasoning with multimodal large language models

    Yuxiang Guo, Faizan Siddiqui, Yang Zhao, Rama Chellappa, and Shao-Yuan Lo. Stimuvar: Spatiotemporal stimuli-aware video affective reasoning with multimodal large language models. arXiv preprint arXiv:2409.00304, 2024. 1, 2

    2024 arXiv

  22. [30]

    Br35h: Brain tumor detection 2020, 2020

    Ahmed Hamada. Br35h: Brain tumor detection 2020, 2020. 6

    2020

  23. [31]

    Madgan: Unsupervised medical anomaly detection gan us- ing multiple adjacent brain mri slice reconstruction

    Changhee Han, Leonardo Rundo, Kohei Murao, Tomoyuki Noguchi, Yuki Shimahara, Zolt ´an ´Ad´am Milacski, Saori Koshino, Evis Sala, Hideki Nakayama, and Shin’ichi Satoh. Madgan: Unsupervised medical anomaly detection gan us- ing multiple adjacent brain mri slice reconstruction. B...

    2021

  24. [32]

    Learning unified reference rep- resentation for unsupervised multi-class anomaly detection

    Liren He, Zhengkai Jiang, Jinlong Peng, Liang Liu, Qian- gang Du, Xiaobin Hu, Wenbing Zhu, Mingmin Chi, Yabiao Wang, and Chengjie Wang. Learning unified reference rep- resentation for unsupervised multi-class anomaly detection. arXiv preprint arXiv:2403.11561, 2024. 1

    2024 arXiv

  25. [33]

    Long-tailed anomaly detection with learnable class names

    Chih-Hui Ho, Kuan-Chuan Peng, and Nuno Vasconcelos. Long-tailed anomaly detection with learnable class names. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12435–12446, 2024

    2024

  26. [34]

    Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection

    Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, Shil- iang Pu, and Hong Zhou. Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8791–8800, 2021. 1

    2021

  27. [35]

    Registration based few-shot anomaly detection

    Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, and Yan-Feng Wang. Registration based few-shot anomaly detection. In European Conference on Computer Vision, pages 303–319. Springer, 2022. 1, 2

    2022

  28. [36]

    Adapting visual-language models for generalizable anomaly detection in medical im- ages

    Chaoqin Huang, Aofan Jiang, Jinghao Feng, Ya Zhang, Xin- chao Wang, and Yanfeng Wang. Adapting visual-language models for generalizable anomaly detection in medical im- ages. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 11375–11385,

  29. [37]

    Towards open-world object- based anomaly detection via self-supervised outlier synthe- sis

    Brian KS Isaac-Medina, Yona Falinie A Gaus, Neelanjan Bhowmik, and Toby P Breckon. Towards open-world object- based anomaly detection via self-supervised outlier synthe- sis. In European Conference on Computer Vision (ECCV),

  30. [38]

    Winclip: Zero- /few-shot anomaly classification and segmentation

    Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer. Winclip: Zero- /few-shot anomaly classification and segmentation. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19606–19616, 2023. 1, 2, 7

    2023

  31. [39]

    Deep learning-based defect detection of metal parts: evaluating current methods in complex condi- tions

    Stepan Jezek, Martin Jonak, Radim Burget, Pavel Dvorak, and Milos Skotak. Deep learning-based defect detection of metal parts: evaluating current methods in complex condi- tions. In 2021 13th International congress on ultra mod- ern telecommunications and control systems and w...

    2021

  32. [40]

    Brain tumor detec- tion using mri images

    Pranita Balaji Kanade and PP Gumaste. Brain tumor detec- tion using mri images. Brain, 3(2):146–150, 2015. 6

    2015

  33. [41]

    Head ct - hemorrhage, 2018

    Felipe Campos Kitamura. Head ct - hemorrhage, 2018. 6

    2018

  34. [42]

    Text-guided variational image generation for industrial anomaly detection and seg- mentation

    Mingyu Lee and Jongwon Choi. Text-guided variational image generation for industrial anomaly detection and seg- mentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26519– 26528, 2024. 1

    2024

  35. [43]

    Zero-shot anomaly detection via batch normalization

    Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, and Stephan Mandt. Zero-shot anomaly detection via batch normalization. Advances in Neural Information Processing Systems, 36, 2024. 1

    2024

  36. [44]

    Llava-onevision: Easy visual task transfer, 2024

    Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer, 2024. 1, 3, 7, 8, 15

    2024

  37. [45]

    LLaV A-med: Training a large language- and-vision assistant for biomedicine in one day

    Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao. LLaV A-med: Training a large language- and-vision assistant for biomedicine in one day. In Thirty- seventh Conference on Neural Information Processi...

    2023

  38. [46]

    Cutpaste: Self-supervised learning for anomaly detection and localization

    Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, and Tomas Pfister. Cutpaste: Self-supervised learning for anomaly detection and localization. In IEEE/CVF conference on computer vision and pattern recognition, 2021. 2

    2021

  39. [47]

    Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models

    Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, and Chunyuan Li. Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models. arXiv preprint arXiv:2407.07895, 2024. 1, 3

    2024 arXiv

  40. [48]

    Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

    Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Interna- 10 tional conference on machine learning, pages 12888–12900. PMLR, 2022. 1, 2

    2022

  41. [49]

    Blip- 2: Bootstrapping language-image pre-training with frozen image encoders and large language models

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip- 2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In Interna- tional conference on machine learning, pages 19730–19742. PMLR, 2023. 2, 4

    2023

  42. [50]

    Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network

    Wenqiao Li, Xiaohao Xu, Yao Gu, Bozhong Zheng, Shenghua Gao, and Yingna Wu. Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat...

    2024

  43. [51]

    Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection

    Xiaofan Li, Zhizhong Zhang, Xin Tan, Chengwei Chen, Yanyun Qu, Yuan Xie, and Lizhuang Ma. Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16838– 16...

    2024

  44. [52]

    Promptad: Zero-shot anomaly detection using text prompts

    Yiting Li, Adam Goodge, Fayao Liu, and Chuan-Sheng Foo. Promptad: Zero-shot anomaly detection using text prompts. In Proceedings of the IEEE/CVF Winter Conference on Ap- plications of Computer Vision, pages 1093–1102, 2024. 1

    2024

  45. [53]

    Open-vocabulary semantic segmentation with mask-adapted clip

    Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, and Diana Marculescu. Open-vocabulary semantic segmentation with mask-adapted clip. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pag...

    2023

  46. [54]

    Rouge: A package for automatic evaluation of summaries

    Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74–81, 2004. 6

    2004

  47. [55]

    Microsoft coco: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceeding...

    2014

  48. [56]

    Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation

    Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, and Xiaofei He. Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco...

    2023

  49. [57]

    Visual instruction tuning

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 1, 2, 3, 5, 6, 16

    2023

  50. [58]

    Improved baselines with visual instruction tuning

    Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296–26306, 2024. 1

    2024

  51. [59]

    Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

    Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024. 3

    2024

  52. [60]

    Real3d- AD: A dataset of point cloud anomaly detection

    Jiaqi Liu, Guoyang Xie, ruitao chen, Xinpeng Li, Jinbao Wang, Yong Liu, Chengjie Wang, and Feng Zheng. Real3d- AD: A dataset of point cloud anomaly detection. In Thirty- seventh Conference on Neural Information Processing Sys- tems Datasets and Benchmarks Track, 2023. 6

    2023

  53. [61]

    Clip-driven universal model for organ segmentation and tumor detection

    Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, and Zongwei Zhou. Clip-driven universal model for organ segmentation and tumor detection. In Proceedings of the IEEE/CVF International Conference on Compute...

    2023

  54. [62]

    Roberta: A robustly optimized bert pretraining approach, 2019

    Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettle- moyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach, 2019. 2

    2019

  55. [63]

    Adversar- ially robust one-class novelty detection

    Shao-Yuan Lo, Poojan Oza, and Vishal M Patel. Adversar- ially robust one-class novelty detection. In IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 2022. 2

    2022

  56. [64]

    SGDR: Stochastic gradi- ent descent with warm restarts

    Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradi- ent descent with warm restarts. In International Conference on Learning Representations, 2017. 15

    2017

  57. [65]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019. 15

    2019

  58. [66]

    Learn to explain: Multimodal reasoning via thought chains for science question answering

    Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507–2521,

  59. [67]

    Video anomaly detection and explanation via large language models

    Hui Lv and Qianru Sun. Video anomaly detection and explanation via large language models. arXiv preprint arXiv:2401.05702, 2024. 1, 2

    2024 arXiv

  60. [68]

    Vt-adl: A vision trans- former network for image anomaly detection and localiza- tion

    Pankaj Mishra, Riccardo Verk, Daniele Fornasier, Claudio Piciarelli, and Gian Luca Foresti. Vt-adl: A vision trans- former network for image anomaly detection and localiza- tion. In 2021 IEEE 30th International Symposium on Indus- trial Electronics (ISIE), pages 01–06. IEEE, 2021. 6

    2021

  61. [69]

    RGI: robust GAN- inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection

    Shancong Mou, Xiaoyi Gu, Meng Cao, Haoping Bai, Ping Huang, Jiulong Shan, and Jianjun Shi. RGI: robust GAN- inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection. In The Eleventh International Conference on Learning Representations, 2023. 1, 2

    2023

  62. [70]

    Reason2drive: Towards interpretable and chain-based reasoning for autonomous driving

    Ming Nie, Renyuan Peng, Chunwei Wang, Xinyue Cai, Jian- hua Han, Hang Xu, and Li Zhang. Reason2drive: Towards interpretable and chain-based reasoning for autonomous driving. In European Conference on Computer Vision, pages 292–308. Springer, 2025. 1

    2025

  63. [71]

    Gpt-4v(ision) system card, 2023

    OpenAI. Gpt-4v(ision) system card, 2023. https:// openai.com/index/gpt-4v-system-card . 5, 8

    2023

  64. [72]

    Gpt-4o system card, 2024

    OpenAI. Gpt-4o system card, 2024. https://openai. com/index/gpt-4o-system-card . 2, 5, 6, 7, 8, 14, 15

    2024

  65. [73]

    Learn- ing transferable visual models from natural language super- vision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learn- ing transferable visual models from natural language super- vision. In International conference on machine learning...

    2021

  66. [74]

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer, 2023. 2

    2023

  67. [75]

    Sentence-bert: Sentence embeddings using siamese bert-networks, 2019

    Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks, 2019. 6

    2019

  68. [76]

    Mean-shifted contrastive loss for anomaly detection

    Tal Reiss and Yedid Hoshen. Mean-shifted contrastive loss for anomaly detection. In Proceedings of the AAAI Con- ference on Artificial Intelligence, pages 2155–2162, 2023. 1

    2023

  69. [77]

    Towards total recall in industrial anomaly detection

    Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Sch¨olkopf, Thomas Brox, and Peter Gehler. Towards total recall in industrial anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14318–14328, 2022. 1, 2

    2022

  70. [78]

    Prompt- guided zero-shot anomaly action recognition using pre- trained deep skeleton features

    Fumiaki Sato, Ryo Hachiuma, and Taiki Sekii. Prompt- guided zero-shot anomaly action recognition using pre- trained deep skeleton features. In Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition, pages 6471–6480, 2023. 1

    2023

  71. [79]

    Mae- day: Mae for few-and zero-shot anomaly-detection

    Eli Schwartz, Assaf Arbelle, Leonid Karlinsky, Sivan Harary, Florian Scheidegger, Sivan Doveh, and Raja Giryes. Mae- day: Mae for few-and zero-shot anomaly-detection. Com- puter Vision and Image Understanding, 241:103958, 2024. 1

    2024

  72. [80]

    Robovqa: Multimodal long-horizon reasoning for robotics

    Pierre Sermanet, Tianli Ding, Jeffrey Zhao, Fei Xia, De- bidatta Dwibedi, Keerthana Gopalakrishnan, Christine Chan, Gabriel Dulac-Arnold, Sharath Maddineni, Nikhil J Joshi, et al. Robovqa: Multimodal long-horizon reasoning for robotics. In 2024 IEEE International Conference on...

    2024

  73. [81]

    A public fabric database for defect detection methods and results

    Javier Silvestre-Blanes, Teresa Albero-Albero, Ignacio Mi- ralles, Rub´en P´erez-Llorens, and Jorge Moreno. A public fabric database for defect detection methods and results. Autex Research Journal, 19(4):363–374, 2019. 6

    2019

  74. [82]

    Generalad: Anomaly detection across domains by attending to distorted features

    Luc PJ Str¨ater, Mohammadreza Salehi, Efstratios Gavves, Cees GM Snoek, and Yuki M Asano. Generalad: Anomaly detection across domains by attending to distorted features. arXiv preprint arXiv:2407.12427, 2024. 1

    2024 arXiv

  75. [83]

    Face-mllm: A large face perception model,

    Haomiao Sun, Mingjie He, Tianheng Lian, Hu Han, and Shiguang Shan. Face-mllm: A large face perception model,

  76. [84]

    An incremental unified framework for small defect inspection

    Jiaqi Tang, Hao Lu, Xiaogang Xu, Ruizheng Wu, Sixing Hu, Tong Zhang, Tsz Wa Cheng, Ming Ge, Ying-Cong Chen, and Fugee Tsung. An incremental unified framework for small defect inspection. In European Conference on Computer Vision, pages 307–324. Springer, 2025. 1

    2025

  77. [85]

    Revisiting reverse distillation for anomaly detection

    Tran Dinh Tien, Anh Tuan Nguyen, Nguyen Hoang Tran, Ta Duc Huy, Soan Duong, Chanh D Tr Nguyen, and Steven QH Truong. Revisiting reverse distillation for anomaly detection. In IEEE/CVF conference on computer vision and pattern recognition, 2023. 2

    2023

  78. [86]

    Clipn for zero-shot ood detection: Teaching clip to say no

    Hualiang Wang, Yi Li, Huifeng Yao, and Xiaomeng Li. Clipn for zero-shot ood detection: Teaching clip to say no. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1802–1812, 2023. 2

    2023

  79. [87]

    Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution, 2024

    Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Jun- yang Lin. Qwen2-vl: Enhancing vision-language model’s ...

    2024

  80. [88]

    Chain-of-thought prompting elicits reasoning in large lan- guage models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large lan- guage models. In Conference on Neural Information Pro- cessing Systems, 2022. 2

    2022

  81. [89]

    Anomaly detection for medical images based on a one-class classification

    Qi Wei, Yinhao Ren, Rui Hou, Bibo Shi, Joseph Y Lo, and Lawrence Carin. Anomaly detection for medical images based on a one-class classification. In Medical Imaging 2018: Computer-Aided Diagnosis , pages 375–380. SPIE,

    2018

  82. [90]

    Diffusion models for medical anomaly detection

    Julia Wolleb, Florentin Bieder, Robin Sandk ¨uhler, and Philippe C Cattin. Diffusion models for medical anomaly detection. In International Conference on Medical image computing and computer-assisted intervention, pages 35–45. Springer, 2022. 1

    2022

  83. [91]

    Funqa: Towards surprising video comprehension

    Binzhu Xie, Sicheng Zhang, Zitang Zhou, Bo Li, Yuanhan Zhang, Jack Hessel, Jingkang Yang, and Ziwei Liu. Funqa: Towards surprising video comprehension. In European Con- ference on Computer Vision, pages 39–57. Springer, 2025. 1

    2025

  84. [92]

    Pushing the limits of fewshot anomaly detection in industry vision: Graphcore

    Guoyang Xie, Jinbao Wang, Jiaqi Liu, Yaochu Jin, and Feng Zheng. Pushing the limits of fewshot anomaly detection in industry vision: Graphcore. In The Eleventh International Conference on Learning Representations, 2023. 1

    2023

  85. [93]

    Emovit: Revolutionizing emotion insights with vi- sual instruction tuning

    Hongxia Xie, Chu-Jun Peng, Yu-Wen Tseng, Hung-Jen Chen, Chan-Feng Hsu, Hong-Han Shuai, and Wen-Huang Cheng. Emovit: Revolutionizing emotion insights with vi- sual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26...

    2024

  86. [94]

    Follow the rules: reasoning for video anomaly detection with large language models

    Yuchen Yang, Kwonjoon Lee, Behzad Dariush, Yinzhi Cao, and Shao-Yuan Lo. Follow the rules: reasoning for video anomaly detection with large language models. arXiv preprint arXiv:2407.10299, 2024. 1, 2

    2024 arXiv

  87. [95]

    Glad: Towards bet- ter reconstruction with global and local adaptive diffusion models for unsupervised anomaly detection

    Hang Yao, Ming Liu, Haolin Wang, Zhicun Yin, Zifei Yan, Xiaopeng Hong, and Wangmeng Zuo. Glad: Towards bet- ter reconstruction with global and local adaptive diffusion models for unsupervised anomaly detection. arXiv preprint arXiv:2406.07487, 2024. 1

    2024 arXiv

  88. [96]

    Dense connector for mllms, 2024

    Huanjin Yao, Wenhao Wu, Taojiannan Yang, YuXin Song, Mengxi Zhang, Haocheng Feng, Yifan Sun, Zhiheng Li, Wanli Ouyang, and Jingdong Wang. Dense connector for mllms, 2024. 17

    2024

  89. [97]

    Hierarchical gaussian mixture normal- izing flow modeling for unified anomaly detection

    Xincheng Yao, Ruoqi Li, Zefeng Qian, Lu Wang, and Chongyang Zhang. Hierarchical gaussian mixture normal- izing flow modeling for unified anomaly detection. arXiv preprint arXiv:2403.13349, 2024. 1

    2024 arXiv

  90. [98]

    A unified model for multi-class anomaly detection

    Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le. A unified model for multi-class anomaly detection. In Advances in Neural Information Pro- cessing Systems, 2022. 1 12

    2022

  91. [99]

    Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection

    Vitjan Zavrtanik, Matej Kristan, and Danijel Skoˇcaj. Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection. In IEEE/CVF international confer- ence on computer vision, 2021. 2

    2021

  92. [100]

    Sigmoid loss for language image pre-training

    Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11975–11986, 2023. 3

    2023

  93. [101]

    Internlm-xcomposer-2.5: A versatile large vision language model supporting long-contextual input and output, 2024

    Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, Songyang Zhang, Wenwei Zhang, Yining Li, Yang Gao, Peng Sun, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Hang Yan, Conghui He, Xingcheng Zhang, Kai Chen, J...

    2024

  94. [102]

    Destseg: Segmentation guided denoising student- teacher for anomaly detection

    Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, and Ting Chen. Destseg: Segmentation guided denoising student- teacher for anomaly detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 2

    2023

  95. [103]

    Mediclip: Adapting clip for few- shot medical image anomaly detection

    Ximiao Zhang, Min Xu, Dehui Qiu, Ruixin Yan, Ning Lang, and Xiuzhuang Zhou. Mediclip: Adapting clip for few- shot medical image anomaly detection. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pages 458–468. Springer, 2024. 1

    2024

  96. [104]

    Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection

    Ximiao Zhang, Min Xu, and Xiuzhuang Zhou. Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16699–16708, 2024. 1

    2024

  97. [105]

    Multimodal chain-of-thought reasoning in language models

    Zhuosheng Zhang, Aston Zhang, Mu Li, hai zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models. Transactions on Machine Learning Research, 2024. 2

    2024

  98. [106]

    Anomaly detection for medi- cal images using self-supervised and translation-consistent features

    He Zhao, Yuexiang Li, Nanjun He, Kai Ma, Leyuan Fang, Huiqi Li, and Yefeng Zheng. Anomaly detection for medi- cal images using self-supervised and translation-consistent features. IEEE Transactions on Medical Imaging, 40(12): 3641–3651, 2021. 1

    2021

  99. [107]

    Navgpt: Explicit reasoning in vision-and-language navigation with large lan- guage models

    Gengze Zhou, Yicong Hong, and Qi Wu. Navgpt: Explicit reasoning in vision-and-language navigation with large lan- guage models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7641–7649, 2024. 1

    2024

  100. [108]

    Learning to prompt for vision-language models

    Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. In- ternational Journal of Computer Vision, 130(9):2337–2348,

  101. [109]

    Vicor: Bridging visual understanding and common- sense reasoning with large language models

    Kaiwen Zhou, Kwonjoon Lee, Teruhisa Misu, and Xin Eric Wang. Vicor: Bridging visual understanding and common- sense reasoning with large language models. In Findings of the Association for Computational Linguistics, 202‘. 1, 2

  102. [110]

    AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection

    Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jim- ing Chen. AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection. In The Twelfth Interna- tional Conference on Learning Representations, 2024. 1, 2, 3, 6, 7

    2024

  103. [111]

    MiniGPT-4: Enhancing vision-language understanding with advanced large language models

    Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mo- hamed Elhoseiny. MiniGPT-4: Enhancing vision-language understanding with advanced large language models. In The Twelfth International Conference on Learning Representa- tions, 2024. 1, 2

    2024

  104. [112]

    Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts

    Jiawen Zhu and Guansong Pang. Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17826– 17836, 2024. 1, 2

    2024

  105. [113]

    Do llms understand visual anomalies? uncov- ering llm’s capabilities in zero-shot anomaly detection

    Jiaqi Zhu, Shaofeng Cai, Fang Deng, Beng Chin Ooi, and Junran Wu. Do llms understand visual anomalies? uncov- ering llm’s capabilities in zero-shot anomaly detection. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 48–57, 2024. 1

    2024

  106. [114]

    Fine-grained abnormality prompt learning for zero- shot anomaly detection, 2024

    Jiawen Zhu, Yew-Soon Ong, Chunhua Shen, and Guansong Pang. Fine-grained abnormality prompt learning for zero- shot anomaly detection, 2024. 7

    2024

  107. [115]

    A scratchedcar

    Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer. Spot-the-difference self-supervised pre- training for anomaly detection and segmentation. In Eu- ropean Conference on Computer Vision , pages 392–408. Springer, 2022. 6, 15 13 A1. Dataset Establishment A...

    2022

  108. [116]

    A scratched car

    Extract CLIP features2. Get cosine similarity3. Remove duplications “A scratched car” GPT-4oNormalAnomaly Image Cleaning Combine Prompt “A scratched car” GPT-4o A noticeable scratch runs horizontally across the car door. The scratch is white, suggesting the paint has been remo...

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.