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Learning time-aware features for action quality assessment
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PRE
AI
DOI：10.1016/j.patrec.2022.04.015.png)
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摘要
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Action quality assessment (AQA) is a task to assess the performance of a human action, which can be widely used in many real-world scenarios such as sport events. Current AQA methods generally extract features from the video and perform regression analysis to obtain the action quality score. In this process, aggregated video features may not reflect different stages of an action, which are important to evaluate an action is good or not. To address this issue, we propose to divide the video into different clips and learn the relationship between them, which may capture the action changes for accurate assessment. Time aware (TA) attention mechanism is used to evaluate this relationship. In the experiment, our proposed method achieves promising results on the MTL-AQA dataset compared with existing AQA methods.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Action quality assessment
Time-aware attention
Video clips
Action quality assessment (AQA)
Time-aware (TA)
Non-local (NL)
Aggregation
Adversarial
Action quality assessment
Time-aware attention
Video clips
Action quality assessment (AQA)
Time-aware (TA)
Non-local (NL)
Aggregation
Adversarial
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
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