SPAR: A Sequential Primacy and Attribution Ranking Framework for Skill Determination

Skill determination in video understanding remains challeng-ing due to the intricate temporal dynamics of skill performance and thecomplex cognitive processes inherently underlying human evaluation. Ex-isting approaches often overlook the progressive evolution of evaluators’cognitive states and the influence of key action stages on overall per-formance. To address this limitation, we propose Sequential Primacyand Attribution Ranking Modeling (SPAR). SPAR captures the tem-poral evolution of cognitive states throughout the evaluation process,enabling iterative refinement of evaluative understanding and producingprogressively enhanced representations of overall performance quality.The framework integrates adaptive weighting based on the global tempo-ral context, emphasizing critical segments through localized interactionmodeling and multi-scale temporal aggregation. To correct inconsisten-cies in intermediate recognition signals, SPAR employs a bias-mitigationloss to ensure a faithful and consistent reflection of intrinsic action qual-ity. Extensive experiments on five public datasets validate the effective-ness and generalization capability of the proposed framework.