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arXiv:2607. 21267v1 Announce Type: new Abstract: Comprehensive basketball video understanding requires resolving not only what event occurs, but also who is responsible and when the key evidence appears.
arXiv:2608. 19723v1 Announce Type: cross Abstract: Streaming video understanding requires models to causally update state as video arrives and organize growing history into semantic units that can evolve, persist, and be recalled under bounded computation and memory.
The paper introduces the Semantic Action Graph, a lightweight domain schema that models a sports match using performer, action, recipient, moment, and state nodes linked by role, temporal, and outcome edges. This structure supports both an agentic pipeline for generating narrated highlights and a visual interface that lets viewers query and inspect the same representation. In a prototype called SportSAGE, 12 soccer fans reported satisfaction with the generated highlights and used the graph interface to search, navigate, and interpret match moments.
arXiv:2608. 19646v1 Announce Type: new Abstract: Visual understanding in sports has emerged as a hot topic in computer vision in recent years.
arXiv:2608.23435v1 Announce Type: cross Abstract: Understanding a basketball game requires recognizing events, localizing actions, identifying players, and relating these to structured game knowledge...
arXiv:2606. 09181v1 Announce Type: cross Abstract: Recent advances in video multimodal models have significantly improved VideoQA performance.