iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

Code

• github.com/vt-vl-lab/iCAN
  Officialtf★ 0
• github.com/zhihou7/VCL
  tf★ 35
• github.com/zhihou7/FCL
  tf★ 16
• github.com/TaiwanRobert/iCAN_for_live_video
  tf★ 0

Abstract

Recent years have witnessed rapid progress in detecting and recognizing individual object instances. To understand the situation in a scene, however, computers need to recognize how humans interact with surrounding objects. In this paper, we tackle the challenging task of detecting human-object interactions (HOI). Our core idea is that the appearance of a person or an object instance contains informative cues on which relevant parts of an image to attend to for facilitating interaction prediction. To exploit these cues, we propose an instance-centric attention module that learns to dynamically highlight regions in an image conditioned on the appearance of each instance. Such an attention-based network allows us to selectively aggregate features relevant for recognizing HOIs. We validate the efficacy of the proposed network on the Verb in COCO and HICO-DET datasets and show that our approach compares favorably with the state-of-the-arts.

Tasks

Benchmark Results

Reproductions