Deep Contextual Attention for Human-Object Interaction Detection

Tiancai Wang, Rao Muhammad Anwer, Muhammad Haris Khan, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao, Jorma Laaksonen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 5694-5702


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.

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author = {Wang, Tiancai and Anwer, Rao Muhammad and Khan, Muhammad Haris and Khan, Fahad Shahbaz and Pang, Yanwei and Shao, Ling and Laaksonen, Jorma},
title = {Deep Contextual Attention for Human-Object Interaction Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}