MoAKE: Toward Unified All-in-One Action Quality Assessment via Mixture of Action Knowledge Experts

• URL: http://arxiv.org/abs/2607.19826v1
• Date: Wed, 22 Jul 2026 06:59:23 GMT
• Title: MoAKE: Toward Unified All-in-One Action Quality Assessment via Mixture of Action Knowledge Experts
• Abstract summary: Action Quality Assessment (AQA) aims to objectively evaluate performance quality from action videos.<n>Most existing methods follow a one-by-one'' paradigm, training a separate model for each action type.<n>We propose a novel Mixture of Action Knowledge Experts (MoAKE) framework, designed to mitigate negative knowledge transfer.
• Score: 24.636480920817736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Action Quality Assessment (AQA) aims to objectively evaluate performance quality from action videos. Most existing methods follow a ``one-by-one'' paradigm, training a separate model for each action type. This setting limits real-world deployment, as it requires prior action-type knowledge to select the corresponding model and suffers from poor generalization across diverse actions. To address these limitations, we study the challenging task of all-in-one AQA, which aims to assess heterogeneous actions within a single unified model. We propose a novel Mixture of Action Knowledge Experts (MoAKE) framework, designed to mitigate negative knowledge transfer caused by large semantic discrepancies among actions. MoAKE learns complementary experts that capture diverse action patterns within a shared semantic space and dynamically aggregates their knowledge to adapt the assessment to the input action. Each expert is tailored with segment-aware prototypes to handle varying temporal lengths, together with an Adaptive Intra- and Inter-Segment Relationship Modeling (AIISRM) module to model multi-granularity temporal dynamics. Furthermore, we establish comprehensive benchmarks for all-in-one as well as zero/few-shot AQA. Extensive experiments on three long-term datasets demonstrate that MoAKE significantly outperforms existing methods in the all-in-one setting, while also achieving consistent generalization on three short-term datasets under zero/few-shot evaluation. Code is available at https://github.com/XuHuangbiao/MoAKE.

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