12232@AAAI

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#1 Action Recognition With Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion [PDF] [Copy] [Kimi] [REL]

Authors: Weiyao Lin, Chongyang Zhang, Ke Lu, Bin Sheng, Jianxin Wu, Bingbing Ni, Xin Liu, Hongkai Xiong

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.

Subject: AAAI.2018 - Vision