  The human-object interaction (HOI) detection task refers to localizing
humans, localizing objects, and predicting the interactions between each
human-object pair. HOI is considered one of the fundamental steps in truly
understanding complex visual scenes. For detecting HOI, it is important to
utilize relative spatial configurations and object semantics to find salient
spatial regions of images that highlight the interactions between human object
pairs. This issue is addressed by the novel self-attention based guided
transformer network, GTNet. GTNet encodes this spatial contextual information
in human and object visual features via self-attention while achieving state of
the art results on both the V-COCO and HICO-DET datasets. Code will be made
available online.
