  We consider the problem of Human-Object Interaction (HOI) Detection, which
aims to locate and recognize HOI instances in the form of <human, action,
object> in images. Most existing works treat HOIs as individual interaction
categories, thus can not handle the problem of long-tail distribution and
polysemy of action labels. We argue that multi-level consistencies among
objects, actions and interactions are strong cues for generating semantic
representations of rare or previously unseen HOIs. Leveraging the compositional
and relational peculiarities of HOI labels, we propose ConsNet, a
knowledge-aware framework that explicitly encodes the relations among objects,
actions and interactions into an undirected graph called consistency graph, and
exploits Graph Attention Networks (GATs) to propagate knowledge among HOI
categories as well as their constituents. Our model takes visual features of
candidate human-object pairs and word embeddings of HOI labels as inputs, maps
them into visual-semantic joint embedding space and obtains detection results
by measuring their similarities. We extensively evaluate our model on the
challenging V-COCO and HICO-DET datasets, and results validate that our
approach outperforms state-of-the-arts under both fully-supervised and
zero-shot settings. Code is available at https://github.com/yeliudev/ConsNet.
