pith. machine review for the scientific record. sign in

arxiv: 2511.03325 · v3 · submitted 2025-11-05 · 💻 cs.CV

SurgViVQA: Temporally-Grounded Video Question Answering for Surgical Scene Understanding

Mauro Orazio Drago , Luca Carlini , Pelinsu Celebi Balyemez , Dennis Pierantozzi , Chiara Lena , Cesare Hassan , Danail Stoyanov , Elena De Momi
show 2 more authors
Sophia Bano Mobarak I. Hoque
This is my paper

Pith reviewed 2026-05-18 01:27 UTC · model grok-4.3

classification 💻 cs.CV
keywords surgical video question answeringtemporal groundingcolonoscopic videovideo understandingmasked video-text encodersurgical scene understandingmedical AIVQA robustness
0
0 comments X

The pith

SurgViVQA shows that a masked video-text encoder improves surgical VideoQA accuracy by capturing motion and tool interactions that image models miss.

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

The paper proposes SurgViVQA to shift surgical question answering from isolated frames to full videos that include ongoing actions. It fuses video features with the question through a Masked Video-Text Encoder that extracts cues such as motion and tool-tissue contact, then feeds the result to a fine-tuned language model for answers. This matters for procedures where timing and movement determine what is happening, yet most existing models ignore the time dimension. The authors also built REAL-Colon-VQA, a colonoscopic video set that adds motion questions and rephrased versions to test real-world robustness. Experiments on this new set and on EndoVis18-VQA report higher keyword accuracy than prior image-based systems.

Core claim

SurgViVQA extends visual reasoning from static images to dynamic surgical scenes by using a Masked Video-Text Encoder to fuse video and question features, thereby capturing temporal cues such as motion and tool-tissue interactions. A fine-tuned large language model then decodes the fused representation into coherent answers. On the newly curated REAL-Colon-VQA dataset, which contains motion-related questions, diagnostic attributes, and out-of-template rephrasings, together with the public EndoVis18-VQA set, the approach raises keyword accuracy by 11 percent over PitVQA on REAL-Colon-VQA and by 9 percent on EndoVis18-VQA while also showing greater robustness under question perturbations.

What carries the argument

The Masked Video-Text Encoder that fuses video frames with question text to capture temporal information such as motion and tool-tissue interactions before an LLM generates the final answer.

If this is right

  • Surgical VideoQA systems can now answer questions that depend on procedural timing and movement rather than single frames.
  • Performance gains appear on both newly collected colonoscopic videos and existing endoscopic datasets.
  • A perturbation study confirms greater robustness when questions are rephrased or altered in meaning.
  • The released REAL-Colon-VQA dataset supplies motion-related and diagnostic questions for future temporally-aware models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Integrating the encoder with live video feeds could support real-time intraoperative assistance during procedures.
  • The same temporal-fusion idea may transfer to other endoscopic or laparoscopic domains where tool motion is central.
  • Joint modeling of motion cues and diagnostic attributes opens the possibility of answering both what is occurring and why it matters.

Load-bearing premise

The Masked Video-Text Encoder can reliably pull out temporal cues like motion and tool interactions from surgical videos without extra explicit temporal modeling or annotations beyond the dataset labels.

What would settle it

A controlled test on surgical videos that contain clear motion events would falsify the claim if SurgViVQA shows no accuracy gain over image-only models precisely on questions that require those motion cues.

read the original abstract

Video Question Answering (VideoQA) in the surgical domain aims to enhance intraoperative understanding by enabling AI models to reason over temporally coherent events rather than isolated frames. Current approaches are limited to static image features, and available datasets often lack temporal annotations, ignoring the dynamics critical for accurate procedural interpretation. We propose SurgViVQA, a surgical VideoQA model that extends visual reasoning from static images to dynamic surgical scenes. It uses a Masked Video--Text Encoder to fuse video and question features, capturing temporal cues such as motion and tool--tissue interactions, which a fine-tuned large language model (LLM) then decodes into coherent answers. To evaluate its performance, we curated REAL-Colon-VQA, a colonoscopic video dataset that includes motion-related questions and diagnostic attributes, as well as out-of-template questions with rephrased or semantically altered formulations to assess model robustness. Experimental validation on REAL-Colon-VQA and the public EndoVis18-VQA dataset shows that SurgViVQA outperforms existing image-based VQA benchmark models, particularly in keyword accuracy, improving over PitVQA by +11\% on REAL-Colon-VQA and +9\% on EndoVis18-VQA. A perturbation study on the questions further confirms improved generalizability and robustness to variations in question phrasing. SurgViVQA and the REAL-Colon-VQA dataset provide a framework for temporally-aware understanding in surgical VideoQA, enabling AI models to interpret dynamic procedural contexts more effectively. Code and dataset available at https://github.com/madratak/SurgViVQA.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents SurgViVQA, a model for temporally-grounded Video Question Answering in surgical scenes. It introduces a Masked Video-Text Encoder to fuse video and question features for capturing temporal cues like motion and tool-tissue interactions, followed by a fine-tuned LLM for answer decoding. The authors curate the REAL-Colon-VQA dataset with motion-related and out-of-template questions, and report that SurgViVQA outperforms image-based VQA models such as PitVQA by +11% keyword accuracy on REAL-Colon-VQA and +9% on EndoVis18-VQA, supported by a perturbation study for robustness.

Significance. If the reported gains are attributable to the temporal components, this work could advance surgical scene understanding by enabling reasoning over dynamic events rather than isolated frames. The public release of the REAL-Colon-VQA dataset and the associated code repository supports reproducibility and further research in the area.

major comments (1)
  1. [Experimental Evaluation] The central performance claims attribute the +11% and +9% keyword accuracy improvements over PitVQA to the temporal fusion in the Masked Video-Text Encoder. However, the manuscript does not include an ablation study that disables the temporal masking and cross-frame interactions (e.g., by encoding frames independently and applying mean pooling) while maintaining the same LLM, training regime, and datasets. Without this, it is unclear whether the gains arise specifically from temporal grounding or from other differences in model architecture or data handling.
minor comments (2)
  1. [Abstract and Results] The abstract and experimental sections do not report statistical significance, error bars, or details on how keyword accuracy was computed, nor the exact train/test splits used for the datasets.
  2. [Method] Clarify the architecture details of the Masked Video-Text Encoder, such as how masking is applied across video frames and the fusion mechanism with the question text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript. We appreciate the referee's careful reading and the opportunity to address the concerns raised. We respond to the major comment below and will revise the manuscript accordingly to strengthen the experimental evaluation.

read point-by-point responses

  1. Referee: The central performance claims attribute the +11% and +9% keyword accuracy improvements over PitVQA to the temporal fusion in the Masked Video-Text Encoder. However, the manuscript does not include an ablation study that disables the temporal masking and cross-frame interactions (e.g., by encoding frames independently and applying mean pooling) while maintaining the same LLM, training regime, and datasets. Without this, it is unclear whether the gains arise specifically from temporal grounding or from other differences in model architecture or data handling.

    Authors: We agree that an ablation isolating the temporal components is necessary to rigorously attribute the reported gains to the Masked Video-Text Encoder. In the revised manuscript we will add this ablation: a variant that encodes frames independently (no temporal masking or cross-frame attention), applies mean pooling over frame features, and uses the identical LLM, training regime, and datasets. We will report the resulting keyword accuracy on both REAL-Colon-VQA and EndoVis18-VQA and discuss how the performance drop relative to the full model supports the contribution of temporal grounding. The perturbation study already present will remain as complementary evidence of robustness. revision: yes

Circularity Check

0 steps flagged

Empirical performance claims on held-out test sets with no self-referential derivation

full rationale

The paper introduces SurgViVQA as a model architecture using a Masked Video-Text Encoder for temporal fusion in surgical VideoQA, then reports keyword accuracy improvements (+11% and +9% over PitVQA) measured on separate test splits of REAL-Colon-VQA and EndoVis18-VQA. These are direct experimental outcomes from training and evaluation on provided datasets rather than any closed-form derivation, fitted parameter renamed as prediction, or self-citation chain that reduces the reported gains to a tautology by construction. No equations or uniqueness theorems are invoked that loop back to the inputs; the central claims remain independently falsifiable via the released code and data.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work relies on standard supervised learning assumptions and the availability of video-question-answer triples; no new physical entities or ad-hoc constants are introduced beyond typical deep-learning hyperparameters.

axioms (2)
  • domain assumption Video frames contain extractable temporal information about motion and tool-tissue interactions that can be captured by a masked encoder without explicit optical flow or tracking modules.
    Invoked in the description of the Masked Video-Text Encoder as the mechanism for capturing temporal cues.
  • domain assumption Fine-tuning a general-purpose LLM on surgical VQA examples produces coherent answers that generalize to rephrased questions.
    Underlying the claim that the LLM decoder yields robust performance after fine-tuning.

pith-pipeline@v0.9.0 · 5859 in / 1494 out tokens · 61663 ms · 2026-05-18T01:27:36.017775+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

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

  1. Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting

    cs.CL 2026-04 unverdicted novelty 4.0

    Reweighting the training loss to emphasize semantically salient tokens lets ophthalmological report generation models reach similar quality with up to ten times less data.

Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · cited by 1 Pith paper · 5 internal anchors

  1. [1]

    Endocrine Reviews44(5), 947–959 (2023)

    Khan, D.Z., Hanrahan, J.G., Baldeweg, S.E., Dorward, N.L., Stoyanov, D., Mar- cus, H.J.: Current and future advances in surgical therapy for pituitary adenoma. Endocrine Reviews44(5), 947–959 (2023)

    work page 2023

  2. [2]

    Gut (2025)

    Carlini, L., Massimi, D., Mori, Y., Antonelli, G., Rizkala, T., Spadaccini, M., Lena, C., Parasa, S., Bisschops, R., Von Renteln, D., et al.: Large language models for detecting colorectal polyps in endoscopic images. Gut (2025)

    work page 2025

  3. [3]

    Endoscopy International Open13(continuous publication) (2025)

    Massimi, D., Carlini, L., Mori, Y., Di Stefano, L., Antonelli, G., Rizkala, T., Spadaccini, M., Sire, R., Alfarone, L., Lena, C., et al.: Large language model for interpreting the paris classification of colorectal polyps. Endoscopy International Open13(continuous publication) (2025)

    work page 2025

  4. [4]

    In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp

    Seenivasan, L., Islam, M., Kannan, G., Ren, H.: Surgicalgpt: end-to-end language- vision gpt for visual question answering in surgery. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 281–290 (2023). Springer

    work page 2023

  5. [5]

    arXiv preprint arXiv:2502.14149 (2025)

    He, R., Khan, D.Z., Mazomenos, E.B., Marcus, H.J., Stoyanov, D., Clarkson, M.J., Islam, M.: Pitvqa++: Vector matrix-low-rank adaptation for open-ended visual question answering in pituitary surgery. arXiv preprint arXiv:2502.14149 (2025)

    work page arXiv 2025

  6. [6]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp

    Seenivasan, L., Islam, M., Krishna, A.K., Ren, H.: Surgical-vqa: Visual ques- tion answering in surgical scenes using transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 33–43 (2022). Springer

    work page 2022

  7. [7]

    In: Proceedings of the First International Workshop on Vision-Language Models for Biomedical Applications, pp

    Gautam, S., Stor˚ as, A.M., Midoglu, C., Hicks, S.A., Thambawita, V., Halvorsen, P., Riegler, M.A.: Kvasir-vqa: A text-image pair gi tract dataset. In: Proceedings of the First International Workshop on Vision-Language Models for Biomedical Applications, pp. 3–12 (2024)

    work page 2024

  8. [8]

    Advances in neural information processing systems35, 10078–10093 (2022)

    Tong, Z., Song, Y., Wang, J., Wang, L.: Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. Advances in neural information processing systems35, 10078–10093 (2022)

    work page 2022

  9. [9]

    ICLR1(2), 3 (2022) 11

    Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.,et al.: Lora: Low-rank adaptation of large language models. ICLR1(2), 3 (2022) 11

    work page 2022

  10. [10]

    Scientific Data11(1), 539 (2024)

    Biffi, C., Antonelli, G., Bernhofer, S., Hassan, C., Hirata, D., Iwatate, M., Maieron, A., Salvagnini, P., Cherubini, A.: Real-colon: A dataset for developing real-world ai applications in colonoscopy. Scientific Data11(1), 539 (2024)

    work page 2024

  11. [11]

    International journal of computer assisted radiology and surgery19(7), 1409–1417 (2024)

    Yuan, K., Kattel, M., Lavanchy, J.L., Navab, N., Srivastav, V., Padoy, N.: Advanc- ing surgical vqa with scene graph knowledge. International journal of computer assisted radiology and surgery19(7), 1409–1417 (2024)

    work page 2024

  12. [12]

    When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA

    Pierantozzi, D., Carlini, L., Drago, M.O., Lena, C., Hassan, C., De Momi, E., Stoyanov, D., Bano, S., Hoque, M.I.: When to trust the answer: Question- aligned semantic neighbour entropy for safer surgical vqa. arXiv preprint arXiv:2511.01458 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  13. [13]

    In: International Conference on Machine Learning, pp

    Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, pp. 12888–12900 (2022). PMLR

    work page 2022

  14. [14]

    Qwen Technical Report

    Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  15. [15]

    MedGemma Technical Report

    Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., et al.: Medgemma technical report. arXiv preprint arXiv:2507.05201 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  16. [16]

    InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

    Zhu, J., Wang, W., Chen, Z., Liu, Z., Ye, S., Gu, L., Tian, H., Duan, Y., Su, W., Shao, J., et al.: Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  17. [17]

    VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

    Zhang, B., Li, K., Cheng, Z., Hu, Z., Yuan, Y., Chen, G., Leng, S., Jiang, Y., Zhang, H., Li, X., et al.: Videollama 3: Frontier multimodal foundation models for image and video understanding. arXiv preprint arXiv:2501.13106 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  18. [18]

    In: Proceedings of the 30th ACM International Conference on Multimedia, pp

    Ma, Y., Xu, G., Sun, X., Yan, M., Zhang, J., Ji, R.: X-clip: End-to-end multi- grained contrastive learning for video-text retrieval. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 638–647 (2022)

    work page 2022

  19. [19]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Arnab, A., Dehghani, M., Heigold, G., Sun, C., Luˇ ci´ c, M., Schmid, C.: Vivit: A video vision transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6836–6846 (2021)

    work page 2021

  20. [20]

    Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: Icml, vol. 2, p. 4 (2021)

    work page 2021

  21. [21]

    In: European Conference on Computer Vision, pp

    Li, K., Li, X., Wang, Y., He, Y., Wang, Y., Wang, L., Qiao, Y.: Videomamba: State space model for efficient video understanding. In: European Conference on Computer Vision, pp. 237–255 (2024). Springer 12

    work page 2024