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International Journal of Computer Vision

Visual Social Relationship Recognition

  • 03-02-2020
Published in:
Authors
Junnan Li
Yongkang Wong
Qi Zhao
Mohan S. Kankanhalli
Published in
International Journal of Computer Vision | Issue 6/2020

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Abstract

Social relationships form the basis of social structure of humans. Developing computational models to understand social relationships from visual data is essential for building intelligent machines that can better interact with humans in a social environment. In this work, we study the problem of visual social relationship recognition in images. We propose a dual-glance model for social relationship recognition, where the first glance fixates at the person of interest and the second glance deploys attention mechanism to exploit contextual cues. To enable this study, we curated a large scale People in Social Context dataset, which comprises of 23,311 images and 79,244 person pairs with annotated social relationships. Since visually identifying social relationship bears certain degree of uncertainty, we further propose an adaptive focal loss to leverage the ambiguous annotations for more effective learning. We conduct extensive experiments to quantitatively and qualitatively demonstrate the efficacy of our proposed method, which yields state-of-the-art performance on social relationship recognition.

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previous article Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning
next article RGB-IR Person Re-identification by Cross-Modality Similarity Preservation
Title
Visual Social Relationship Recognition
Authors
Junnan Li
Yongkang Wong
Qi Zhao
Mohan S. Kankanhalli
Publication date
03-02-2020
Publisher
Springer US
Published in
International Journal of Computer Vision / Issue 6/2020
Print ISSN: 0920-5691
Electronic ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-020-01295-1
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Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning

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RGB-IR Person Re-identification by Cross-Modality Similarity Preservation

Fine-Grained Person Re-identification

Real-Time Multi-person Motion Capture from Multi-view Video and IMUs

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