Human-Object Relation Network for Action Recognition in Still Images

Surrounding object information has been widely used for action recognition. However, the relation between human and object, as an important cue, is usually ignored in the still image action recognition field. In this paper, we propose a novel approach for action recognition. The key to ours is a human-object relation module. By using the appearance as well as the spatial location of human and object, the module can compute the pair-wise relation information between human and object to enhance features for action classification and can be trained jointly with our action recognition network. Experimental results on two popular datasets demonstrate the effectiveness of the proposed approach. Moreover, our method yields the new state-of-the-art results of 92.8% and 94.6% mAP on the PASCAL VOC 2012 Action and Stanford 40 Actions datasets respectively. Ablation study and visualization confirm the proposed method can model and utilize the human-object relation for action recognition.