  The recent advances in instance-level detection tasks lay strong foundation
for genuine comprehension of the visual scenes. However, the ability to fully
comprehend a social scene is still in its preliminary stage. In this work, we
focus on detecting human-object interactions (HOIs) in social scene images,
which is demanding in terms of research and increasingly useful for practical
applications. To undertake social tasks interacting with objects, humans direct
their attention and move their body based on their intention. Based on this
observation, we provide a unique computational perspective to explore human
intention in HOI detection. Specifically, the proposed human intention-driven
HOI detection (iHOI) framework models human pose with the relative distances
from body joints to the object instances. It also utilizes human gaze to guide
the attended contextual regions in a weakly-supervised setting. In addition, we
propose a hard negative sampling strategy to address the problem of
mis-grouping. We perform extensive experiments on two benchmark datasets,
namely V-COCO and HICO-DET. The efficacy of each proposed component has also
been validated.
