  Human-object interaction detection is an important and relatively new class
of visual relationship detection tasks, essential for deeper scene
understanding. Most existing approaches decompose the problem into object
localization and interaction recognition. Despite showing progress, these
approaches only rely on the appearances of humans and objects and overlook the
available context information, crucial for capturing subtle interactions
between them. We propose a contextual attention framework for human-object
interaction detection. Our approach leverages context by learning
contextually-aware appearance features for human and object instances. The
proposed attention module then adaptively selects relevant instance-centric
context information to highlight image regions likely to contain human-object
interactions. Experiments are performed on three benchmarks: V-COCO, HICO-DET
and HCVRD. Our approach outperforms the state-of-the-art on all datasets. On
the V-COCO dataset, our method achieves a relative gain of 4.4% in terms of
role mean average precision ($mAP_{role}$), compared to the existing best
approach.
