  Human-Object Interaction (HOI) detection has received considerable attention
in the context of scene understanding. Despite the growing progress on
benchmarks, we realize that 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 benchmark datasets results in under-fitting on these instances.
To address these problems, in this paper, 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 two challenging datasets
- HICO-DET and V-COCO. The results demonstrate that the proposed method can
surpass existing approaches by a large margin, resulting in new
state-of-the-art performance.
