CTM: Collaborative Temporal Modeling for Action Recognition

• URL: http://arxiv.org/abs/2002.03152v1
• Date: Sat, 8 Feb 2020 12:14:02 GMT
• Title: CTM: Collaborative Temporal Modeling for Action Recognition
• Authors: Qian Liu, Tao Wang, Jie Liu, Yang Guan, Qi Bu, Longfei Yang
• Abstract summary: We propose a Collaborative Temporal Modeling (CTM) block to learn temporal information for action recognition. CTM includes two collaborative paths: a spatial-aware temporal modeling path, and a spatial-unaware temporal modeling path. Experiments on several popular action recognition datasets demonstrate that CTM blocks bring the performance improvements on 2D CNN baselines.
• Score: 11.467061749436356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of digital multimedia, video understanding has become an important field. For action recognition, temporal dimension plays an important role, and this is quite different from image recognition. In order to learn powerful feature of videos, we propose a Collaborative Temporal Modeling (CTM) block (Figure 1) to learn temporal information for action recognition. Besides a parameter-free identity shortcut, as a separate temporal modeling block, CTM includes two collaborative paths: a spatial-aware temporal modeling path, which we propose the Temporal-Channel Convolution Module (TCCM) with unshared parameters for each spatial position (H*W) to build, and a spatial-unaware temporal modeling path. CTM blocks can seamlessly be inserted into many popular networks to generate CTM Networks and bring the capability of learning temporal information to 2D CNN backbone networks, which only capture spatial information. Experiments on several popular action recognition datasets demonstrate that CTM blocks bring the performance improvements on 2D CNN baselines, and our method achieves the competitive results against the state-of-the-art methods. Code will be made publicly available.

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