An Expressive Deep Model for Human Action Parsing from A Single Image
This addresses the challenge of action recognition in still images for computer vision applications, but it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of recognizing human actions from single images by developing an expressive deep model that integrates human layout and surrounding contexts, and it outperforms state-of-the-art approaches.
This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images. Its main challenges lie in the large variations in human poses and appearances, as well as the lack of temporal motion information. Addressing these problems, we propose to develop an expressive deep model to naturally integrate human layout and surrounding contexts for higher level action understanding from still images. In particular, a Deep Belief Net is trained to fuse information from different noisy sources such as body part detection and object detection. To bridge the semantic gap, we used manually labeled data to greatly improve the effectiveness and efficiency of the pre-training and fine-tuning stages of the DBN training. The resulting framework is shown to be robust to sometimes unreliable inputs (e.g., imprecise detections of human parts and objects), and outperforms the state-of-the-art approaches.