Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

• URL: http://arxiv.org/abs/2203.04476v1
• Date: Wed, 9 Mar 2022 01:30:57 GMT
• Title: Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework
• Authors: Xiaodong Chen, Xinchen Liu, Wu Liu, Kun Liu, Dong Wu, Yongdong Zhang, Tao Mei
• Abstract summary: Part-level Action Parsing (PAP) aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Our framework achieves state-of-the-art performance and outperforms existing methods over a 31.10% ROC score.
• Score: 108.70949305791201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over a 31.10% ROC score.

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