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Polysemy Deciphering Network for Human-Object Interaction Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12365)

Abstract

Human-Object Interaction (HOI) detection is important in human-centric scene understanding. Existing works typically assume that the same verb in different HOI categories has similar visual characteristics, while ignoring the diverse semantic meanings of the verb. To address this issue, in this paper, we propose a novel Polysemy Deciphering Network (PD-Net), which decodes the visual polysemy of verbs for HOI detection in three ways. First, PD-Net augments human pose and spatial features for HOI detection using language priors, enabling the verb classifiers to receive language hints that reduce the intra-class variation of the same verb. Second, we introduce a novel Polysemy Attention Module (PAM) that guides PD-Net to make decisions based on more important feature types according to the language priors. Finally, the above two strategies are applied to two types of classifiers for verb recognition, i.e., object-shared and object-specific verb classifiers, whose combination further relieves the verb polysemy problem. By deciphering the visual polysemy of verbs, we achieve the best performance on both HICO-DET and V-COCO datasets. In particular, PD-Net outperforms state-of-the-art approaches by 3.81% mAP in the Known-Object evaluation mode of HICO-DET. Code of PD-Net is available at https://github.com/MuchHair/PD-Net.

Keywords

Human-object interaction Verb polysemy Attention model 

Notes

Acknowledgement

Changxing Ding is the corresponding author. This work was supported by the NSF of China under Grant 61702193, the Science and Technology Program of Guangzhou under Grant 201804010272, the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X183, the Fundamental Research Funds for the Central Universities of China under Grant 2019JQ01, and ARC FL-170100117.

Supplementary material

504476_1_En_5_MOESM1_ESM.pdf (7.8 mb)
Supplementary material 1 (pdf 8018 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.UBTECH Sydney AI Centre, School of Computer Science, Faculty of EngineeringThe University of SydneyDarlingtonAustralia

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