TAM: Temporal Adaptive Module for Video Recognition

• URL: http://arxiv.org/abs/2005.06803v3
• Date: Wed, 18 Aug 2021 12:19:06 GMT
• Title: TAM: Temporal Adaptive Module for Video Recognition
• Authors: Zhaoyang Liu, Limin Wang, Wayne Wu, Chen Qian, Tong Lu
• Abstract summary: temporal adaptive module (bf TAM) generates video-specific temporal kernels based on its own feature map. Experiments on Kinetics-400 and Something-Something datasets demonstrate that our TAM outperforms other temporal modeling methods consistently.
• Score: 60.83208364110288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video data is with complex temporal dynamics due to various factors such as camera motion, speed variation, and different activities. To effectively capture this diverse motion pattern, this paper presents a new temporal adaptive module ({\bf TAM}) to generate video-specific temporal kernels based on its own feature map. TAM proposes a unique two-level adaptive modeling scheme by decoupling the dynamic kernel into a location sensitive importance map and a location invariant aggregation weight. The importance map is learned in a local temporal window to capture short-term information, while the aggregation weight is generated from a global view with a focus on long-term structure. TAM is a modular block and could be integrated into 2D CNNs to yield a powerful video architecture (TANet) with a very small extra computational cost. The extensive experiments on Kinetics-400 and Something-Something datasets demonstrate that our TAM outperforms other temporal modeling methods consistently, and achieves the state-of-the-art performance under the similar complexity. The code is available at \url{ https://github.com/liu-zhy/temporal-adaptive-module}.

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