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Parallel Reasoning Network for Human-Object Interaction Detection

2023-01-09 17:00:34
Huan Peng, Fenggang Liu, Yangguang Li, Bin Huang, Jing Shao, Nong Sang, Changxin Gao
arXiv_CV
             
Abstract

Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and their corresponding interactions by using a predictor. Using only one shared predictor cannot differentiate the attentive field of instance-level prediction and relation-level prediction. To solve this problem, we propose a new transformer-based method named Parallel Reasoning Network(PR-Net), which constructs two independent predictors for instance-level localization and relation-level understanding. The former predictor concentrates on instance-level localization by perceiving instances' extremity regions. The latter broadens the scope of relation region to reach a better relation-level semantic understanding. Extensive experiments and analysis on HICO-DET benchmark exhibit that our PR-Net effectively alleviated this problem. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.

Abstract (translated)

URL

https://arxiv.org/abs/2301.03510

PDF

https://arxiv.org/pdf/2301.03510.pdf

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