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Action Quality Assessment(AQA) is important for action understanding andresolving the task poses unique challenges due to subtle visual differences.Existing state-of-the-art methods typically rely on the holistic videorepresentations for score regression or ranking, which limits thegeneralization to capture fine-grained intra-class variation. To overcome theabove limitation, we propose a temporal parsing transformer to decompose theholistic feature into temporal part-level representations. Specifically, weutilize a set of learnable queries to represent the atomic temporal patternsfor a specific action. Our decoding process converts the frame representationsto a fixed number of temporally ordered part representations. To obtain thequality score, we adopt the state-of-the-art contrastive regression based onthe part representations. Since existing AQA datasets do not provide temporalpart-level labels or partitions, we propose two novel loss functions on thecross attention responses of the decoder: a ranking loss to ensure thelearnable queries to satisfy the temporal order in cross attention and asparsity loss to encourage the part representations to be more discriminative.Extensive experiments show that our proposed method outperforms prior work onthree public AQA benchmarks by a considerable margin.

Action Quality Assessment with Temporal Parsing Transformer