  Since detecting and recognizing individual human or object are not adequate
to understand the visual world, learning how humans interact with surrounding
objects becomes a core technology. However, convolution operations are weak in
depicting visual interactions between the instances since they only build
blocks that process one local neighborhood at a time. To address this problem,
we learn from human perception in observing HOIs to introduce a two-stage
trainable reasoning mechanism, referred to as GID block. GID block breaks
through the local neighborhoods and captures long-range dependency of pixels
both in global-level and instance-level from the scene to help detecting
interactions between instances. Furthermore, we conduct a multi-stream network
called GID-Net, which is a human-object interaction detection framework
consisting of a human branch, an object branch and an interaction branch.
Semantic information in global-level and local-level are efficiently reasoned
and aggregated in each of the branches. We have compared our proposed GID-Net
with existing state-of-the-art methods on two public benchmarks, including
V-COCO and HICO-DET. The results have showed that GID-Net outperforms the
existing best-performing methods on both the above two benchmarks, validating
its efficacy in detecting human-object interactions.
