{"ID":2866858,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18527","arxiv_id":"2509.18527","title":"FERA: A Pose-Based Framework for Rule-Grounded Multimedia Decision Support with a Foil Fencing Case Study","abstract":"Multimedia decision support requires more than recognition; it requires explicit state estimates that can be checked against rules, audited by humans, and consumed by downstream decision logic. We present the FEncing Referee Assistant (FERA), a pose-based framework for this setting, and study it through foil fencing, where decisions depend on fast bilateral motion and right-of-way rules. The framework separates canonical participant tracking, kinematic tokenization, calibrated temporal perception, a compact structured decision layer, and an explanation-oriented retrieval interface. We also release an audited benchmark with adjudicated labels and fixed folds for reproducible evaluation. Under a shared protocol, a lightweight lifted-depth sidecar strengthens the best graph-based perception model, while a compact structured classifier on the fixed two-dimensional token stream reaches 0.624 accuracy and a 0.632 macro-averaged F1 score on the final Left / Right / None decision. The case study supports a broader design lesson: keep the boundary between perception and rule application explicit, preserve uncertainty, and choose the perception front end according to the downstream operating point.","short_abstract":"Multimedia decision support requires more than recognition; it requires explicit state estimates that can be checked against rules, audited by humans, and consumed by downstream decision logic. We present the FEncing Referee Assistant (FERA), a pose-based framework for this setting, and study it through foil fencing, w...","url_abs":"https://arxiv.org/abs/2509.18527","url_pdf":"https://arxiv.org/pdf/2509.18527v5","authors":"[\"Ziwen Chen\",\"Zhong Wang\"]","published":"2025-09-23T01:47:44Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}