Spatial attention based visual semantic learning for action recognition in still images.

Visual semantic parts play crucial roles in still image-based action recognition. A majority of existing methods require additional manual annotations such as human bounding boxes and predefined body parts besides action labels to learn action related visual semantic parts. However, labeling these manual annotations is rather time-consuming and labor-intensive. Moreover, not all manual annotations are effective when recognizing a specific action. Some of them can be irrelevant and even misguided. To address these limitations, this paper proposes a multi-stage deep learning method called Spatial Attention based Action Mask Networks (SAAM-Nets). The proposed method does not need any additional annotations besides action labels to obtain action-specific visual semantic parts. Instead, we propose a spatial attention layer injected in a convolutional neural network to create a specific action mask for each image with only action labels. Moreover, based on the action mask, we propose a region selection strategy to generate a semantic bounding box containing action-specific semantic parts. Furthermore, to effectively combine the information of the whole scene and the sematic box, two feature attention layers are adopted to obtain more discriminative representations. Experiments on four benchmark datasets have demonstrated that the proposed method can achieve promising performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]