# An Expressive Deep Model for Human Action Parsing from A Single Image

arXiv: https://arxiv.org/abs/1502.00501 · HTML: https://mlsanity.com/paper/1502.00501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2015.

## Built on by (most cited first, 1 of 1)

- [Towards Simple and Accurate Human Pose Estimation with Stair Network](https://mlsanity.com/paper/2202.09115/) (2022)

## Abstract (arXiv)

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.