MoAKE framework offers unified approach to action quality assessment

Researchers have introduced MoAKE, a novel framework for unified Action Quality Assessment (AQA) that can evaluate diverse actions within a single model. Unlike previous methods that require separate models for each action type, MoAKE employs a mixture of experts, each specializing in different action patterns. This approach mitigates negative knowledge transfer and dynamically aggregates expert knowledge to adapt to the input action. MoAKE also incorporates segment-aware prototypes and an Adaptive Intra- and Inter-Segment Relationship Modeling module to handle varying temporal lengths and model multi-granularity temporal dynamics. The framework demonstrates significant improvements in all-in-one AQA and strong generalization capabilities in zero/few-shot evaluations. AI

IMPACT This research could lead to more versatile and deployable action quality assessment systems, reducing the need for specialized models for each action type.

RANK_REASON This is a research paper detailing a new framework for action quality assessment. [lever_c_demoted from research: ic=1 ai=1.0]

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• Action Quality Assessment
• Adaptive Intra- and Inter-Segment Relationship Modeling
• AIISRM
• Mixture of Action Knowledge Experts
• Moake

• paper
• other

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