Pith. sign in

REVIEW 4 major objections 6 minor 22 references

Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read RASO claims that a weakly supervised pipeline, trained on tags mined from 2,200 surgical lecture videos, improves zero-shot mAP on four benchmarks by up to 10.6 points over prior methods.

desk verdict A genuinely useful data pipeline, but the zero-shot results on cholecystectomy benchmarks are not trustworthy until the WebSurg/IRCAD overlap is checked. read the letter →

arxiv 2501.15326 v2 pith:3ZU3E2NA submitted 2025-01-25 cs.CV

Jiajie Li , Brian R Quaranto , Chenhui Xu , Ishan Mishra , Ruiyang Qin , Dancheng Liu , Peter C W Kim , Jinjun Xiong This is my paper · ORCID
classification cs.CV
keywords surgicalobjectrecognitionweaklysupervisedlearningzero-shotvision-languagepretrainingtemporalattentionfusionlecturevideosopen-setimagetagging
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 surgical object recognizer can be pretrained entirely on weakly supervised data mined from narrated surgery videos, removing the need for manual annotation at scale. The model, RASO, is trained on 901K frames paired with 3.6 million tags extracted from voiceover transcripts and automated vision-language annotations. In zero-shot tests on four standard benchmarks, it improves mean average precision over prior surgical vision-language models by 2.9, 4.5, 10.6, and 7.2 points respectively. If the claim holds, large collections of instructional medical videos become a viable training source for open-set surgical recognition, which matters in a domain where annotated data is scarce and expensive to produce.

What carries the argument

The load-bearing object is the weakly supervised data generation pipeline: a data engine that collects surgery lecture videos, transcribes voiceovers with WhisperX, filters out slides, and samples frames; and a label engine that extracts surgical tags via biomedical named-entity recognition, parses verb-noun action triplets from sentences, and augments them with automated vision-language annotations, yielding tag-image-text triplets. Training uses a RAM-style architecture (RAM is a general image-tagging model) composed of a Swin image encoder, a frozen CLIP text encoder that embeds the tag list, a tag decoder, and a training-only text decoder that reconstructs captions to align visual and textual representations. The temporal-attention fusion layer aggregates frame features along time, which the paper shows improves verb recognition (74.5 vs 68.5 mAP) and overall video mAP, and the open-vocabulary mechanism generates embeddings for unseen tags on the fly.

What would settle it

Sample a set of lecture frames, have clinicians mark which tags are visibly present, and compare those against the pipeline's tags; if visible-grounding accuracy is low, or if shuffling the transcript-to-frame pairing leaves zero-shot mAP nearly unchanged, the reported gains would be explained by text-side shortcuts rather than visual recognition.

Watch

Extended reading notes

Core claim

RASO establishes that weakly supervised tag-image-text triplets, generated automatically from 2,200 unannotated surgical lecture videos, are sufficient to train a foundation model that recognizes surgical objects in an open-set manner. The pipeline produces 2,066 unique surgical tags across 901K images, and a temporal-attention fusion layer extends the image tag decoder to video inputs. On zero-shot recognition, RASO reaches 22.6 mAP on CholecT50, 37.8 on Cholec80, 58.8 on RSS, and 20.0 on GraSP, exceeding the strongest prior baseline in each case. In the supervised CholecT50 triplet task, RASO reaches 57.5 mAP, above the Rendezvous baseline of 56.4, while the video mode is roughly four times faster than frame-by-frame inference. The paper presents these results as evidence that a weakly supervised data engine can substitute for manual annotation in a specialized visual domain.

Load-bearing premise

The load-bearing premise is that each tag extracted from a surgeon's voiceover actually names something visible in the frame it is paired with, so the model learns visual recognition rather than lexical correlations.

Editorial extensions

If this is right

  • Zero-shot surgical object recognition improves on four public benchmarks without manual annotations, so RASO can be applied directly to new surgical scenes and unseen classes.
  • The weakly supervised data engine lowers annotation cost: pretraining and fine-tuning complete in about eight hours on eight A6000 GPUs.
  • The temporal-attention fusion layer makes video recognition more accurate and faster than per-frame processing, reporting 61.3 vs 58.8 mAP and roughly four times lower latency.
  • Open-vocabulary inference lets the model recognize tags outside its 2,066-tag training vocabulary by embedding them with the frozen CLIP text encoder.
  • The same tag-image-text generation recipe is claimed to transfer to other vertical domains where narrated videos exist but manual annotations are scarce.

Reading between the lines

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

  • If the transcript-to-frame alignment is reliable, scaling the pipeline to a much larger lecture-video corpus could push zero-shot accuracy further, and the resulting tags could seed a reusable surgical taxonomy for captioning, question answering, or grounded segmentation.
  • A direct testable extension would be to apply the same data and label engines to other video-rich clinical settings, such as endoscopy or interventional radiology, to see whether the weakly supervised gains transfer across procedure types.
  • The paper leaves open whether the zero-shot gains come primarily from the lecture-video pretraining or from the automated vision-language annotations added during fine-tuning; a per-tag breakdown of the fine-tuning contribution would isolate that effect.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper introduces RASO, a foundation model for open-set surgical object recognition built on the RAM architecture, trained with weakly supervised tag-image-text triplets automatically generated from 2,200 surgical lecture videos. The pipeline uses WhisperX ASR, a biomedical NER model, scene-graph parsing, and GPT-4o VLM annotations to produce 3.6 million tag annotations across 2,066 unique tags. RASO reports zero-shot mAP improvements of 2.9, 4.5, 10.6, and 7.2 points over prior methods on CholecT50, Cholec80, RSS, and GraSP respectively, and state-of-the-art supervised results on CholecT50 action-triplet recognition, including a temporal-attention fusion mechanism for video input.

Significance. If the zero-shot results hold under scrutiny, the paper makes a substantial contribution: it offers a scalable, low-annotation data-generation recipe, an open-vocabulary surgical recognition model, and a temporal fusion mechanism with a reported speed/accuracy advantage. The authors commit to releasing code, model, and data, which would aid reproducibility. However, the validity of the central zero-shot claim currently depends on two unverified points: the absence of overlap between the WebSurg pretraining corpus and the IRCAD cholecystectomy benchmarks, and the use of a genuinely zero-shot evaluation protocol rather than test-set-optimized thresholds. The supervised results and ablations are informative, but the headline claim needs these analyses before the paper can be accepted.

major comments (4)
  1. [Section 4.1, Table 2] The zero-shot claim on CholecT50 and Cholec80 is threatened by possible corpus overlap. Section 4.1 states that pretraining data was sourced from 'platforms like WebSurg', and WebSurg is the e-surgical platform of IRCAD, the same group that produced the Cholec80 and CholecT50 laparoscopic cholecystectomy benchmarks. The paper reports no video-level deduplication, no exclusion of benchmark videos, and no analysis of frame-level overlap between the 901K pretraining frames and the benchmark test frames. If any WebSurg clips are the same recordings as the Cholec80 or CholecT50 videos, the reported zero-shot gains on those datasets would reflect memorization rather than generalization. This is directly testable: the authors should perform video- and frame-level near-duplicate detection between the pretraining corpus and the benchmark test sets, exclude overlapping content, and re-report the zero-shot results after exclusion.
  2. [Section 5.1, Table 2] The reported Precision, Recall, and F-beta values are not zero-shot because the decision threshold is fitted to the test set. Section 5.1 states: 'we apply the same rule to find the best threshold for each method which maximizes the Fβ=0.5 score.' This means each method's threshold is chosen using the test labels, so the F-beta and Precision/Recall numbers in Table 2 are oracle-threshold results, not predictions under a fixed zero-shot protocol. The threshold is a free parameter that is tuned on the evaluation data. The authors should fix thresholds on a validation set or report mAP, which is threshold-free, as the primary zero-shot metric; the current F-beta comparisons should be presented as upper bounds.
  3. [Section 5.2, Table 2] The text misstates the Cholec80 comparison for the no-fine-tuning variant. The paper says: 'even without fine-tuning with GPT-4o annotations, RASO (w/o FT) outperforms all other approaches across datasets, except for Cholec80, where it is marginally outperformed by SurgVLP.' Table 2 shows that on Cholec80, SurgVLP achieves mAP 33.3 and Fβ=0.5 31.1, whereas RASO (w/o FT) achieves mAP 25.0 and Fβ=0.5 27.6. This is a substantial gap, not a marginal one. The sentence should be corrected to accurately describe the quantitative results in the table.
  4. [Sections 4.1, 4.2, Appendix A.1] The paper does not provide any evaluation of whether the automatically extracted voiceover tags are visually grounded in the frames they are paired with. The only quantitative annotation-quality check is for GPT-4o-generated annotations in Appendix A.1, Table 5, which reports agreement rates between 0.19 and 0.35 against clinician annotations; the definition of this agreement rate and the chance baseline are not stated. Since the entire pretraining signal comes from these tags, the authors should either include a sampled human evaluation of tag-image alignment for the speech-derived tags, or provide an analysis showing that the model's predictions are not driven by lexical shortcuts (e.g., tag co-occurrence with transcript words). Without this, the paper's central mechanism remains unverified.
minor comments (6)
  1. [Section 5.1] There is a typo in the subsection heading: 'Evaluaiton Metrics' should be 'Evaluation Metrics'.
  2. [Section 5.1] The text says 'we choose Fβ=0.5 to balance the precision and recall', but β=0.5 weights precision more heavily than recall. The wording should say 'to emphasize precision over recall'.
  3. [Section 4.1 and Figure 3] Figure 3 shows a tag with the note 'helmet trunk (manually removed)', indicating that manual filtering is applied, but the paper does not describe the manual filtering procedure, the number of tags removed, or the qualification of the people performing it. Please provide these details in the data generation section.
  4. [References] There are duplicate references for Nwoye et al. 2020 (a, b, c) that all point to the same paper. These should be consolidated to avoid citation confusion.
  5. [Section 4.1] The abstract reports 3.6 million tag annotations and 2,066 unique tags, while Section 4.1 states 1,455 tags for pretraining and 611 for fine-tuning (totaling 2,066). It should be clarified whether the 3.6 million count includes both stages and how the fine-tuning tag set is constructed.
  6. [Table 2, Section 5.2] The paper does not report variance or confidence intervals for the mAP improvements. Given the small test sizes (e.g., RSS has 997 images), the authors should provide error bars or significance tests for the main zero-shot comparisons.

Circularity Check

0 steps flagged · score 0.0 of 10

No within-paper circularity: the zero-shot mAP claims are not fitted to benchmark labels, and the only self-citation is non-load-bearing related work.

full rationale

RASO's central claim is that pretraining on weakly supervised WebSurg lecture triplets and fine-tuning on GPT-4o-generated tags transfers to four benchmarks. The paper does not train or tune on the benchmark labels for the zero-shot experiments: Section 3.2 states that for zero-shot experiments the authors "exclusively relied on VLM-generated data," and Section 5.1 confirms the supervised task is the only one fine-tuned on CholecT50's training split. The reported mAP improvements therefore are not forced by fitting the benchmark annotations. There is one self-citation (LLaVA-Surg, Li et al. 2024) but it appears only in related work as an example of surgical video QA and carries no load in the derivation. The only fitted evaluation quantity is the precision/recall threshold, selected per method on the test set to maximize F-beta (Section 5.1); this affects the F-beta columns but not the headline mAP numbers, so it does not make the main claim circular. The data-overlap risk between WebSurg pretraining and the IRCAD-origin benchmarks is a real external validity threat, but the paper contains no quote or exhibited duplication that would make the zero-shot result equivalent to memorization by construction; without such evidence it is a contamination hypothesis, not a circular reduction. The tag-image-text generation relies on the standard weak-supervision assumption that voiceover entities are visually present, which is an assumption about data quality, not a self-referential equation. No definition, equation, or fitted parameter in the manuscript reduces to the target benchmarks, so the derivation chain is self-contained as written.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central empirical claims depend on assumptions about weak supervision quality and data domain match. No new physical entities are introduced.

free parameters (2)
  • per-benchmark decision threshold = not reported (optimized on test set)
    Section 5.1: 'we apply the same rule to find the best threshold for each method which maximizes the F-beta=0.5 score'; this threshold is selected on the test split, affecting P/R/F-beta/mAP numbers.
  • tag confidence threshold at inference = not reported
    Section 3.1: 'a predefined threshold is applied to identify the recognized objects'; the value is not specified and is likely tuned.
assumptions (5)
  • domain assumption Voiceover transcripts in surgical lectures describe the visual content of the corresponding frames.
    Section 4.1 pairs frames with WhisperX transcriptions; if the narration refers to off-screen content, tags become misaligned.
  • domain assumption NER-extracted noun phrases and SceneGraphParser action triplets correspond to objects visible in the frames.
    Section 4.2 builds the tag vocabulary from transcripts; a noun like 'patient' or 'incision' may be spoken but not visible.
  • domain assumption GPT-4o tag annotations are accurate enough to serve as weak supervision.
    Appendix A.1 reports agreement rates of 0.19 to 0.35 with clinician tags on 20 frequent tags, which is low; the paper still uses these annotations in fine-tuning.
  • domain assumption Pretraining data from WebSurg does not substantially overlap with the evaluation benchmarks.
    The paper never analyzes overlap between the 2,200 lecture videos and CholecT50, Cholec80, GraSP, and RSS, all of which are cholecystectomy or robotic surgery videos.
  • domain assumption CLIP text embeddings for unseen tags transfer to surgical concepts.
    Open-vocabulary inference in Section 3.1 relies on CLIP text encoder embeddings for tags not seen in training.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data." pith.science (2026). https://pith.science/paper/3ZU3E2NA

@misc{pith2026250115326,
  author       = {Pith},
  title        = {Pith review of: Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3ZU3E2NA}},
  note         = {Machine review of arXiv:2501.15326}
}
read the original abstract

We present RASO, a foundation model designed to Recognize Any Surgical Object, offering robust open-set recognition capabilities across a broad range of surgical procedures and object classes, in both surgical images and videos. RASO leverages a novel weakly-supervised learning framework that generates tag-image-text pairs automatically from large-scale unannotated surgical lecture videos, significantly reducing the need for manual annotations. Our scalable data generation pipeline gathers 2,200 surgical procedures and produces 3.6 million tag annotations across 2,066 unique surgical tags. Our experiments show that RASO achieves improvements of 2.9 mAP, 4.5 mAP, 10.6 mAP, and 7.2 mAP on four standard surgical benchmarks, respectively, in zero-shot settings, and surpasses state-of-the-art models in supervised surgical action recognition tasks. Code, model, and demo are available at https://ntlm1686.github.io/raso.

Figures

Figures reproduced from arXiv: 2501.15326 by the authors.

Figure 1
Figure 1. Comparison of surgical object recognition performance across different models and clin [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. RASO Architecture. RASO includes (1) an image encoder to extract visual features from [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The weakly supervised pretraining data pipeline (top-left). Surgical lecture videos, in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison on Video Inference. Supervised Video Recognition. We compare two approaches for surgical video object recog￾nition: RASO (video) and RASO (image-based). RASO (video) is the default approach that employs the temporal fusion mechanism that processes se￾quences…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

22 extracted references · 9 canonical work pages

  1. [5]

    Whisperx: Time-accurate speech transcription of long-form audio

    Max Bain, Jaesung Huh, Tengda Han, and Andrew Zisserman. Whisperx: Time-accurate speech transcription of long-form audio. INTERSPEECH 2023,

    work page 2023

  2. [6]

    Language models are few-shot learners

    Tom B Brown. Language models are few-shot learners. arXiv preprint arXiv:2005.14165,

    arXiv 2005

  3. [8]

    Pubmedclip: How much does clip benefit visual question answering in the medical domain? In Findings of the Association for Computa- tional Linguistics: EACL 2023, pp

    Sedigheh Eslami, Christoph Meinel, and Gerard De Melo. Pubmedclip: How much does clip benefit visual question answering in the medical domain? In Findings of the Association for Computa- tional Linguistics: EACL 2023, pp. 1151–1163,

    work page 2023

  4. [9]

    Deep residual learning for image recog- nition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. arxiv e-prints. arXiv preprint arXiv:1512.03385, 10,

    arXiv

  5. [12]

    A comprehensive study of gpt-4v’s multimodal capabilities in medical imaging

    Yingshu Li, Yunyi Liu, Zhanyu Wang, Xinyu Liang, Lingqiao Liu, Lei Wang, Leyang Cui, Zhaopeng Tu, Longyue Wang, and Luping Zhou. A comprehensive study of gpt-4v’s multimodal capabilities in medical imaging. medRxiv, pp. 2023–11,

    work page 2023

  6. [13]

    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, Proceedings, Part V 13, pp. 740–755. Springer,

    2014

  7. [14]

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499,

    arXiv

  8. [16]

    doi: 10.18653/v1/W19-5034

    Association for Computational Lin- guistics. doi: 10.18653/v1/W19-5034. URL https://www.aclweb.org/anthology/ W19-5034. 11 Published as a conference paper at ICLR 2025 Chinedu Innocent Nwoye and Nicolas Padoy. Data splits and metrics for benchmarking methods on surgical action triplet datasets. arXiv preprint arXiv:2204.05235,

    arXiv 2025

Show all 22 references
  1. [17]

    Recognition of instrument-tissue interactions in endoscopic videos via action triplets

    Chinedu Innocent Nwoye, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, and Nicolas Padoy. Recognition of instrument-tissue interactions in endoscopic videos via action triplets. In Medical Image Computing and Computer Assisted Intervention– MIC...

    2020

  2. [18]

    websurg.com

    URLhttps://www. websurg.com. Accessed: 2024-05-29. Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, and Wei-Ying Ma. Unified visual-semantic embeddings: Bridging vision and language with structured meaning representa- tions. In Proceedings of the IEEE/CVF C...

    2024

  3. [19]

    Learning multi-modal representations by watching hundreds of surgical video lectures

    Kun Yuan, Vinkle Srivastav, Tong Yu, Joel Lavanchy, Pietro Mascagni, Nassir Navab, and Nicolas Padoy. Learning multi-modal representations by watching hundreds of surgical video lectures. arXiv preprint arXiv:2307.15220,

    arXiv

  4. [20]

    Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al

    URLhttps://arxiv.org/abs/ 2405.10075. Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al. Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432,

    arXiv

  5. [21]

    Surgicalsam: Efficient class promptable surgical instrument segmentation

    12 Published as a conference paper at ICLR 2025 Wenxi Yue, Jing Zhang, Kun Hu, Yong Xia, Jiebo Luo, and Zhiyong Wang. Surgicalsam: Efficient class promptable surgical instrument segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 6890–6898,

    2025

  6. [22]

    Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

    Sheng Zhang, Yanbo Xu, Naoto Usuyama, Hanwen Xu, Jaspreet Bagga, Robert Tinn, Sam Pre- ston, Rajesh Rao, Mu Wei, Naveen Valluri, et al. Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv preprint arXiv:2303.00915,

    arXiv

  7. [2004]

    Llava-surg: Towards multimodal surgical assistant via structured surgical video learning

    Jiajie Li, Garrett Skinner, Gene Yang, Brian R Quaranto, Steven D Schwaitzberg, Peter CW Kim, and Jinjun Xiong. Llava-surg: Towards multimodal surgical assistant via structured surgical video learning. arXiv preprint arXiv:2408.07981,

    arXiv

  8. [2015]

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

    Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929,

    2010 arXiv

  9. [2019]

    2018 robotic scene segmentation challenge

    Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, et al. 2018 robotic scene segmentation challenge. arXiv preprint arXiv:2001.11190,

    2018 arXiv

  10. [2020]

    Matis: Masked- attention transformers for surgical instrument segmentation

    Nicol´as Ayobi, Alejandra P´erez-Rond´on, Santiago Rodr´ıguez, and Pablo Arbel´aez. Matis: Masked- attention transformers for surgical instrument segmentation. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE,

    2023

  11. [2021]

    Decoupled weight decay regularization

    I Loshchilov. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101,

    arXiv

  12. [2022]

    2017 robotic instrument segmentation challenge

    Max Allan, Alex Shvets, Thomas Kurmann, Zichen Zhang, Rahul Duggal, Yun-Hsuan Su, Nicola Rieke, Iro Laina, Niveditha Kalavakonda, Sebastian Bodenstedt, et al. 2017 robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426,

    2017 arXiv

  13. [2023]

    Pixel-wise recognition for holistic surgical scene understanding

    Nicol´as Ayobi, Santiago Rodr ´ıguez, Alejandra P ´erez, Isabela Hern ´andez, Nicol ´as Aparicio, Eug´enie Dessevres, Sebasti´an Pe˜na, Jessica Santander, Juan Ignacio Caicedo, Nicol´as Fern´andez, et al. Pixel-wise recognition for holistic surgical scene understanding. arXiv ...

    arXiv

  14. [2024]

    Ophnet: A large-scale video benchmark for ophthalmic surgical workflow understanding

    10 Published as a conference paper at ICLR 2025 Ming Hu, Peng Xia, Lin Wang, Siyuan Yan, Feilong Tang, Zhongxing Xu, Yimin Luo, Kaimin Song, Jurgen Leitner, Xuelian Cheng, et al. Ophnet: A large-scale video benchmark for ophthalmic surgical workflow understanding. In European ...

    2025

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.