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Recently, the soft attention mechanism, which was originally proposed inlanguage processing, has been applied in computer vision tasks like imagecaptioning. This paper presents improvements to the soft attention model bycombining a convolutional LSTM with a hierarchical system architecture torecognize action categories in videos. We call this model the ConvolutionalHierarchical Attention Model (CHAM). The model applies a convolutionaloperation inside the LSTM cell and an attention map generation process torecognize actions. The hierarchical architecture of this model is able toexplicitly reason on multi-granularities of action categories. The proposedarchitecture achieved improved results on three publicly available datasets:the UCF sports dataset, the Olympic sports dataset and the HMDB51 dataset.

CHAM: action recognition using convolutional hierarchical attention model