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New Hierarchical GRU Model Anticipates Football Actions with 17.91% mAP

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Hierarchical GRU Model Anticipates Football Actions with 17.91% mAP

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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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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Parthsarthi Rawat ·

    Hierarchical GRU with Input-Conditioned Slot Queries for Ball Action Anticipation

    arXiv:2606.14730v1 Announce Type: new Abstract: We present a hierarchical model for ball action anticipation in football broadcast video. Given a 30-second observation window, the system predicts actions occurring in the subsequent 5-second window across 10 classes. A shared loca…

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