On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos
Temporal vision-language models (TVLMs) offer a reusable, prompt-based interface for surgical video understanding, yet, their robustness under clinically realistic acquisition artifacts in endoscopy remains insufficiently characterized. In practice, degradations such as defocus, haze, motion blur, noise, cautery smoke, and packet loss introduce structured distribution shifts which may compromise video-text alignment. We study the robustness of temporal VLMs under such shifts caused by corruptions in clip frames. We introduce Endo-C6, a compact corruption benchmark of six endoscopy-realistic pe
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “On the Robustness of Temporal Vision-Language Models for Sur” ≈ “VioletVision-3B””
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “On the Robustness of Temporal Vision-Language Models for Sur” ≈ “pytorch/vision””
- FuzzyOverlapping authors or contributors · 62%openai/openai-agents-python →
“Shared author/contributor keys: bilal”
- LinkedLinked via arxiv author · 85%Darakshan Rashid →
“On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos”
- LinkedLinked via arxiv author · 85%Raza Imam →
“On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos”
- LinkedLinked via arxiv author · 85%Ufaq Khan →
“On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos”
- LinkedLinked via arxiv author · 85%Muhammad Bilal →
“On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos”
- LinkedLinked via arxiv author · 85%Shazad Ashraf →
“On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos”