Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
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.
Cite
@article{arxiv.2010.11757,
title = {Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition},
author = {Chun-Fu Chen and Rameswar Panda and Kandan Ramakrishnan and Rogerio Feris and John Cohn and Aude Oliva and Quanfu Fan},
journal= {arXiv preprint arXiv:2010.11757},
year = {2021}
}
Comments
CVPR 2021 camera-ready version. Codes and models are available on https://github.com/IBM/action-recognition-pytorch