GID-Net: Detecting Human-Object Interaction with Global and Instance Dependency

TL;DR

GID-Net introduces a novel two-stage reasoning mechanism with GID blocks to effectively capture long-range dependencies for improved human-object interaction detection in images.

Contribution

The paper proposes GID-Net, a multi-stream network with GID blocks that model global and instance-level dependencies, advancing HOI detection accuracy.

Findings

Outperforms state-of-the-art on V-COCO and HICO-DET benchmarks.

Effectively captures long-range pixel dependencies.

Demonstrates improved interaction detection performance.

Abstract

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.