New Hierarchical GRU Model Anticipates Football Actions with 17.91% mAP

Researchers have developed a novel hierarchical model for anticipating ball actions in football broadcasts. The system utilizes a Transformer to encode clip-level features and a GRU to aggregate temporal context, predicting actions within a 5-second window based on a 30-second observation. This approach incorporates frequency-reweighted Hungarian matching to favor rare action classes and Gaussian soft targets for temporal supervision, achieving 17.91% mAP on the SoccerNet Ball Action Anticipation benchmark. AI

IMPACT This model advances AI's ability to predict events in real-time video, potentially impacting sports analytics and automated broadcasting.

RANK_REASON The cluster describes a new academic paper detailing a novel model for a specific AI task (ball action anticipation). [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• CORE Recommender
• DagsHub
• ESPN FC
• gated recurrent unit
• Gaussian soft targets
• Gotit.pub
• Hugging Face
• Hungarian matching
• Influence Flower
• ScienceCast
• SoccerNet Ball Action Anticipation
• transformer

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