  Human-Object Interaction (HOI) detection plays a crucial role in activity
understanding. Though significant progress has been made, interactiveness
learning remains a challenging problem in HOI detection: existing methods
usually generate redundant negative H-O pair proposals and fail to effectively
extract interactive pairs. Though interactiveness has been studied in both
whole body- and part- level and facilitates the H-O pairing, previous works
only focus on the target person once (i.e., in a local perspective) and
overlook the information of the other persons. In this paper, we argue that
comparing body-parts of multi-person simultaneously can afford us more useful
and supplementary interactiveness cues. That said, to learn body-part
interactiveness from a global perspective: when classifying a target person's
body-part interactiveness, visual cues are explored not only from
herself/himself but also from other persons in the image. We construct
body-part saliency maps based on self-attention to mine cross-person
informative cues and learn the holistic relationships between all the
body-parts. We evaluate the proposed method on widely-used benchmarks HICO-DET
and V-COCO. With our new perspective, the holistic global-local body-part
interactiveness learning achieves significant improvements over
state-of-the-art. Our code is available at
https://github.com/enlighten0707/Body-Part-Map-for-Interactiveness.
