Recent years have witnessed rapid progress in detecting and recognizing individual object instances. To understand the situation in a scene, however, computers need to recognize how humans interact with surrounding objects. In this paper, we tackle the challenging task of detecting human-object interactions (HOI). Our core idea is that the appearance of a person or an object instance contains informative cues on which relevant parts of an image to attend to for facilitating interaction prediction. To exploit this cue, we propose an instance-centric attention module that learns to dynamically highlight regions in an image conditioned on the appearance of each instance. Such an attention-based network allows us to selectively aggregate features relevant for recognizing HOIs. We validate the efficacy of the proposed network on the Verb in COCO and HICO-DET datasets and show that our approach compares favorably with the state-of-the-arts.

@inproceedings{gao2018ican,
    author    = {Gao, Chen and Zou, Yuliang and Huang, Jia-Bin},
    title     = {iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection},
    booktitle = {British Machine Vision Conference},
    year      = {2018}
}

• • Yu-Wei Chao, Yunfan Liu, Xieyang Liu, Huayi Zeng, and Jia Deng, “Learning to Detect Human-Object Interactions”, In WACV, 2018.
• • Georgia Gkioxari, Ross Girshick, Piotr Dollár, and Kaiming He, “Detecting and Recognizing Human-Object Interactions”, In CVPR, 2018.