Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deepbased 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.
Lin et al. REF propose a coarse-to-fine CNN-LSTM based network (CO2FI) which extracts features from different convolutional layers and integrates them by a LSTM network.
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion
ArXiv
ArXiv
Computer Science