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Shanghai Jiao Tong University
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Player-centric modeling in basketball video analysis reveals that traditional methods miss critical interactions, leading to significant performance improvements with the new PlayNet framework.
Over 1,100 submissions reveal groundbreaking advancements in sports video understanding, with new methods pushing the boundaries of action prediction and localization.
Entity-aware comparative reasoning can be learned from routine clinical data, leading to significant improvements in diagnostic accuracy and retrieval performance in radiology.
Static benchmarks fail to predict LLM performance in dynamic clinical settings, with top models only achieving 60.4% of expert criteria in real-world simulations.
Ditch slow, 2D motion proxies: GMOS directly segments moving objects from RGB video in 3D space and time, achieving state-of-the-art speed and accuracy.
