Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition

• URL: http://arxiv.org/abs/2010.11757v4
• Date: Mon, 29 Mar 2021 14:33:42 GMT
• Title: Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
• Authors: Chun-Fu Chen, Rameswar Panda, Kandan Ramakrishnan, Rogerio Feris, John Cohn, Aude Oliva, Quanfu Fan
• Abstract summary: In recent years, a number of approaches based on 2D or 3D convolutional neural networks (CNN) have emerged for video action recognition. We develop an unified framework for both 2D-CNN and 3D-CNN action models. We then conduct an effort towards a large-scale analysis involving over 300 action recognition models.
• Score: 26.006191751270393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, a number of approaches based on 2D or 3D convolutional neural networks (CNN) have emerged for video action recognition, achieving state-of-the-art results on several large-scale benchmark datasets. In this paper, we carry out in-depth comparative analysis to better understand the differences between these approaches and the progress made by them. To this end, we develop an unified framework for both 2D-CNN and 3D-CNN action models, which enables us to remove bells and whistles and provides a common ground for fair comparison. We then conduct an effort towards a large-scale analysis involving over 300 action recognition models. Our comprehensive analysis reveals that a) a significant leap is made in efficiency for action recognition, but not in accuracy; b) 2D-CNN and 3D-CNN models behave similarly in terms of spatio-temporal representation abilities and transferability. Our codes are available at https://github.com/IBM/action-recognition-pytorch.

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