DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning

• URL: http://arxiv.org/abs/2105.12085v1
• Date: Tue, 25 May 2021 17:09:57 GMT
• Title: DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
• Authors: Wenhao Wu, Yuxiang Zhao, Yanwu Xu, Xiao Tan, Dongliang He, Zhikang Zou, Jin Ye, Yingying Li, Mingde Yao, Zichao Dong, Yifeng Shi
• Abstract summary: We coined Kinetic-range and short-range temporal modeling as key aspects of video recognition. In this paper, we introduce Dynamic Segment Aggregation (DSA) module to capture relationship among snippets. Our proposed DSA module is shown to benefit various video recognition models significantly.
• Score: 29.182482776910152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition. Most of the state-of-the-arts focus on short-range spatio-temporal modeling and then average multiple snippet-level predictions to yield the final video-level prediction. Thus, their video-level prediction does not consider spatio-temporal features of how video evolves along the temporal dimension. In this paper, we introduce a novel Dynamic Segment Aggregation (DSA) module to capture relationship among snippets. To be more specific, we attempt to generate a dynamic kernel for a convolutional operation to aggregate long-range temporal information among adjacent snippets adaptively. The DSA module is an efficient plug-and-play module and can be combined with the off-the-shelf clip-based models (i.e., TSM, I3D) to perform powerful long-range modeling with minimal overhead. The final video architecture, coined as DSANet. We conduct extensive experiments on several video recognition benchmarks (i.e., Mini-Kinetics-200, Kinetics-400, Something-Something V1 and ActivityNet) to show its superiority. Our proposed DSA module is shown to benefit various video recognition models significantly. For example, equipped with DSA modules, the top-1 accuracy of I3D ResNet-50 is improved from 74.9% to 78.2% on Kinetics-400. Codes will be available.

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