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arXiv · 1711.07430

Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion

Weiyao Lin·Yang Mi·Jianxin Wu·Ke Lu·Hongkai Xiong
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Abstract

Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams. We first introduce a coarse-to-fine network which extracts shared deep features at different action class granularities and progressively integrates them to obtain a more accurate feature representation for input actions. We further introduce an asynchronous fusion network. It fuses information from different streams by asynchronously integrating stream-wise features at different time points, hence better leveraging the complementary information in different streams. Experimental results on action recognition benchmarks demonstrate that our approach achieves the state-of-the-art performance.

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Weiyao Lin, Yang Mi, Jianxin Wu, Ke Lu, Hongkai Xiong. 2017-11-20. Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion. https://arxiv.org/abs/1711.07430

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