Deep Contextual Attention for Human-Object Interaction Detection

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 ($mA{P}_{role}$), compared to the existing best approach.

Accepted at ICCV 2019

References in corpus (5)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• R-FCN: Object Detection via Region-based Fully Convolutional Networks
• Object Detectors Emerge in Deep Scene CNNs
• Visual Semantic Role Labeling
• Interact as You Intend: Intention-Driven Human-Object Interaction Detection

Cited by in corpus (10)

• HOI Analysis: Integrating and Decomposing Human-Object Interaction
• Learning Human-Object Interaction Detection using Interaction Points
• HOTR: End-to-End Human-Object Interaction Detection with Transformers
• Visual Compositional Learning for Human-Object Interaction Detection
• Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
• Dual ResGCN for Balanced Scene GraphGeneration
• Contextual Heterogeneous Graph Network for Human-Object Interaction Detection
• Glance and Gaze: Inferring Action-aware Points for One-Stage Human-Object Interaction Detection
• HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos
• Zero-Shot Human-Object Interaction Recognition via Affordance Graphs