Effective Actor-centric Human-object Interaction Detection

TL;DR

This paper introduces an actor-centric framework for human-object interaction detection that leverages non-local features and a novel composition strategy, significantly improving accuracy in complex scenes with multiple humans and objects.

Contribution

The proposed method uniquely combines actor-guided non-local features with a pixel-wise interaction area prediction and a center-point indexing composition strategy, advancing HOI detection performance.

Findings

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

More robust in scenes with multiple persons and objects.

Improves detection accuracy in complex interaction scenarios.

Abstract

While Human-Object Interaction(HOI) Detection has achieved tremendous advances in recent, it still remains challenging due to complex interactions with multiple humans and objects occurring in images, which would inevitably lead to ambiguities. Most existing methods either generate all human-object pair candidates and infer their relationships by cropped local features successively in a two-stage manner, or directly predict interaction points in a one-stage procedure. However, the lack of spatial configurations or reasoning steps of two- or one- stage methods respectively limits their performance in such complex scenes. To avoid this ambiguity, we propose a novel actor-centric framework. The main ideas are that when inferring interactions: 1) the non-local features of the entire image guided by actor position are obtained to model the relationship between the actor and context, and then 2) we use an object branch to generate pixel-wise interaction area prediction, where the interaction area denotes the object central area. Moreover, we also use an actor branch to get interaction prediction of the actor and propose a novel composition strategy based on center-point indexing to generate the final HOI prediction. Thanks to the usage of the non-local features and the partly-coupled property of the human-objects composition strategy, our proposed framework can detect HOI more accurately especially for complex images. Extensive experimental results show that our method achieves the state-of-the-art on the challenging V-COCO and HICO-DET benchmarks and is more robust especially in multiple persons and/or objects scenes.