Multi-Temporal Convolutions for Human Action Recognition in Videos

• URL: http://arxiv.org/abs/2011.03949v2
• Date: Wed, 31 Mar 2021 15:02:49 GMT
• Title: Multi-Temporal Convolutions for Human Action Recognition in Videos
• Authors: Alexandros Stergiou and Ronald Poppe
• Abstract summary: We present a novel temporal-temporal convolution block that is capable of extracting at multiple resolutions. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture.
• Score: 83.43682368129072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective extraction of temporal patterns is crucial for the recognition of temporally varying actions in video. We argue that the fixed-sized spatio-temporal convolution kernels used in convolutional neural networks (CNNs) can be improved to extract informative motions that are executed at different time scales. To address this challenge, we present a novel spatio-temporal convolution block that is capable of extracting spatio-temporal patterns at multiple temporal resolutions. Our proposed multi-temporal convolution (MTConv) blocks utilize two branches that focus on brief and prolonged spatio-temporal patterns, respectively. The extracted time-varying features are aligned in a third branch, with respect to global motion patterns through recurrent cells. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture. This introduces a substantial reduction in computational costs. Extensive experiments on Kinetics, Moments in Time and HACS action recognition benchmark datasets demonstrate competitive performance of MTConvs compared to the state-of-the-art with a significantly lower computational footprint.

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