Sports-video understanding is moving beyond event recognition toward explaining how actions collectively shape match progression, however, existing tennis-video methods either perceive individual strokes without modeling their tactical dependencies or generate high-level analyses without grounding them in the underlyin...
Yifan Mei, Qin-Ling Shi, Chang-Li Wu et al.· 0 citations
Experiments show that current MLLMs struggle particularly on questions requiring the integration of multiple capabilities, whereas BasketballSkills outperforms them, highlighting the effectiveness of explicitly composing domain-specific capabilities for comprehensive basketball understanding.
Yi-Rong Hu, Jia-Yuan Rao, Yu Zhang et al.· 1 citation
This work proposes ScopeMamba-YOLO, built around an off-path, zero-gated selective-scanning principle that decouples contextual modeling from the convolutional stream, and shows consistent improvements on AI-TOD, especially for very-tiny and tiny objects.
Jun-Jie Fan, Yi-Jun Mai, Lin-Duo Wei et al.· 0 citations
Each task and its evaluation protocol is described, the challenge leaderboards are presented, and the leading submissions are summarized, with the aim of documenting the current state of each task as measured on held-out challenge data.
A. Cioppa, Silvio Giancola, Haakan Ardo et al.· 1 citation
WorldCupArena is presented, a dynamic benchmark for language models and deep-research agents that can be reused for future leagues and cups, and shows only small gains in result and exact-score accuracy, but a clearer gain in Scoreline.
Zhaokai Wang, T. Gui, Jiayuan Rao et al.· arXiv.org· 1 citation· ⚡1
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
