Distance Matters in Human-Object Interaction Detection
Guangzhi Wang, Yangyang Guo, Yongkang Wong, Mohan Kankanhalli
Abstract
Human-Object Interaction (HOI) detection has received considerable attention in the context of scene understanding. Despite the growing progress, we realize existing methods often perform unsatisfactorily on distant interactions, where the leading causes are two-fold: 1) Distant interactions are by nature more difficult to recognize than close ones. A natural scene often involves multiple humans and objects with intricate spatial relations, making the interaction recognition for distant human-object largely affected by complex visual context. 2) Insufficient number of distant interactions in datasets results in under-fitting on these instances. To address these problems, we propose a novel two-stage method for better handling distant interactions in HOI detection. One essential component in our method is a novel Far Near Distance Attention module. It enables information propagation between humans and objects, whereby the spatial distance is skillfully taken into consideration. Besides, we devise a novel Distance-Aware loss function which leads the model to focus more on distant yet rare interactions. We conduct extensive experiments on HICO-DET and V-COCO datasets. The results show that the proposed method surpass existing methods significantly, leading to new state-of-the-art results.