Deep Contextual Attention for Human-Object Interaction Detection

Citations Over TimeTop 10% of 2019 papers

Abstract

Human-object interaction detection is an important and relatively new class of visual relationship detection tasks, essential for deeper scene understanding. Most existing approaches decompose the problem into object localization and interaction recognition. Despite showing progress, these approaches only rely on the appearances of humans and objects and overlook the available context information, crucial for capturing subtle interactions between them. We propose a contextual attention framework for human-object interaction detection. Our approach leverages context by learning contextually-aware appearance features for human and object instances. The proposed attention module then adaptively selects relevant instance-centric context information to highlight image regions likely to contain human-object interactions. Experiments are performed on three benchmarks: V-COCO, HICO-DET and HCVRD. Our approach outperforms the state-of-the-art on all datasets. On the V-COCO dataset, our method achieves a relative gain of 4.4% in terms of role mean average precision (mAP role ), compared to the existing best approach.

Related Papers

• → Intelligent Detection of Missing and Unattended Objects in Complex Scene of Surveillance Videos(2012)10 cited
• → Object Recognition Method for Industrial Intelligent Robot(2013)7 cited
• → Towards Object Detection from Motion(2019)1 cited
• → Object extraction from stereo vision using continuity of disparity map(2012)
• → 2A1-S02 Unknown Object Detection and Picking Based on Impacting and Keypoint Tracking(2015)