Pairwise Body-Part Attention for Recognizing Human-Object Interactions

Hao-Shu Fang, Jinkun Cao, Yu-Wing Tai, Cewu Lu; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 51-67

In human-object interactions (HOI) recognition, conventional methods consider the human body as a whole and pay a uniform attention to the entire body region. They ignore the fact that normally, human interacts with an object by using some parts of the body. In this paper, we argue that different body parts should be paid with different attention in HOI recognition, and the correlations between different body parts should be further considered. This is because our body parts always work collaboratively. We propose a new pairwise body-part attention model which can learn to focus on crucial parts, and their correlations for HOI recognition. A novel attention based feature selection method and a feature representation scheme that can capture pairwise correlations between body parts are introduced in the model. Our proposed approach achieved 10% relative improvement in mAP over the state-of-the-art results in HOI recognition on the HICO dataset. We will make our model and source codes publicly available.