AI RESEARCH
SurgViVQA: Temporally-Grounded Video Question Answering for Surgical Scene Understanding
arXiv CS.CV
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ArXi:2511.03325v3 Announce Type: replace 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.