TimeProVe proposes a propose-then-verify framework using lightweight action-based candidate evidence generation followed by targeted VLM verification for efficient long video temporal reasoning, achieving 7.3% improvement on OTB with 75% fewer VLM calls.
Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) , series =
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Cataract-LMM is a new multi-source dataset of 3000 annotated phacoemulsification videos enabling benchmarks for phase recognition, scene segmentation, interaction tracking, and automated skill assessment.
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TimeProVe: Propose, then Verify for Efficient Long Video Temporal Reasoning in Activities of Daily Living
TimeProVe proposes a propose-then-verify framework using lightweight action-based candidate evidence generation followed by targeted VLM verification for efficient long video temporal reasoning, achieving 7.3% improvement on OTB with 75% fewer VLM calls.
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Cataract-LMM Large-Scale Multi-Source Multi-Task Benchmark for Deep Learning in Surgical Video Analysis
Cataract-LMM is a new multi-source dataset of 3000 annotated phacoemulsification videos enabling benchmarks for phase recognition, scene segmentation, interaction tracking, and automated skill assessment.