{"slug": "soccernet-2026-player-centric-ball-action-spotting-per-player-attention-with", "title": "SoccerNet 2026 Player-Centric Ball Action Spotting: Per-Player Attention with Agreement-Based Ensembling", "summary": "Researchers submitted a system to the SoccerNet 2026 Player-Centric Ball Action Spotting challenge that uses a two-stage pipeline with per-player attention and agreement-based ensembling, improving Macro-F1 from 48.6 to 58.94.", "body_md": "arXiv:2606.28389v1 Announce Type: new\nAbstract: We present our submission to the SoccerNet 2026 Player-Centric Ball Action Spotting challenge, which uses a two-stage pipeline: a Track-Aware Action Detector (TAAD) produces per-player action logits from broadcast video, and a Denoising Sequence Transduction (DST) transformer converts game-state features and TAAD logits into structured event sequences. We improve the TAAD with a temporal transformer that adds cross-frame context, alongside several training fixes. For the DST stage, we introduce a two-stage per-player attention mechanism operating on game-state features, and show that a spatial-first attention ordering (cross-player attention before temporal attention) improves validation Macro-F1 by 1.87%. To exploit architectural diversity, we train four model variants and combine them with a Weighted Event Fusion ensemble that applies agreement filtering to suppress single-model false positives while preserving recall, plus a dedicated exception for the rare tackle class. Our final system improves the challenge Macro-F1 from a baseline of 48.6 to 58.94.", "url": "https://wpnews.pro/news/soccernet-2026-player-centric-ball-action-spotting-per-player-attention-with", "canonical_source": "https://arxiv.org/abs/2606.28389", "published_at": "2026-06-30 04:00:00+00:00", "updated_at": "2026-06-30 04:24:33.053987+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "neural-networks"], "entities": ["SoccerNet", "Track-Aware Action Detector", "Denoising Sequence Transduction", "Weighted Event Fusion"], "alternates": {"html": "https://wpnews.pro/news/soccernet-2026-player-centric-ball-action-spotting-per-player-attention-with", "markdown": "https://wpnews.pro/news/soccernet-2026-player-centric-ball-action-spotting-per-player-attention-with.md", "text": "https://wpnews.pro/news/soccernet-2026-player-centric-ball-action-spotting-per-player-attention-with.txt", "jsonld": "https://wpnews.pro/news/soccernet-2026-player-centric-ball-action-spotting-per-player-attention-with.jsonld"}}