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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

TOOL · arXiv cs.CV English(EN) · 8h

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

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.
RESEARCH · arXiv cs.CV English(EN) · 1w · [2 sources]

Leveraging Metric Depth for Relative Depth Prediction

Researchers have developed a novel method for predicting relative depth in monocular images, specifically for football scenarios. Their approach utilizes the zero-shot capabilities of large-scale pre-trained models to infer metric depth, which aids in more accurate relative depth estimation. This technique was applied to the 2025 SoccerNet Monocular Depth Estimation Competition Challenge, achieving a score of 2.68 x 10^-3 on the challenge set. AI

IMPACT This method could improve depth estimation in specialized visual domains, aiding applications like sports analytics and augmented reality.