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SoccerNet challenge system uses GNNs and fine-tuning for action spotting

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an enhanced system for the SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge. Their approach builds upon existing FOOTPASS baselines by incorporating gradient checkpointing for efficient fine-tuning, fusing graph neural network (GNN) outputs with visual features, and applying square-root frequency class weighting to balance imbalanced training data. The system achieved a Macro F1 score of 0.548 on the test set and 0.446 on the challenge set. AI

IMPACT This research advances AI capabilities in sports analytics by improving player action recognition in soccer.

RANK_REASON This is a research paper detailing a system for a specific challenge, including technical extensions and performance metrics.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

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

    SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines

    arXiv:2606.09679v1 Announce Type: new Abstract: We describe our system for the SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge, which requires predicting who performs which action and when, across eight classes in broadcast soccer. Building on the three FOOTPASS base…

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

    SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines

    We describe our system for the SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge, which requires predicting who performs which action and when, across eight classes in broadcast soccer. Building on the three FOOTPASS baselines [1] (TAAD, TAAD+GNN, and TAAD+DST), we con…

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