Multi-Centroid Representation Network for Domain Adaptive Person Re-ID

• URL: http://arxiv.org/abs/2112.11689v1
• Date: Wed, 22 Dec 2021 06:40:21 GMT
• Title: Multi-Centroid Representation Network for Domain Adaptive Person Re-ID
• Authors: Yuhang Wu, Tengteng Huang, Haotian Yao, Chi Zhang, Yuanjie Shao, Chuchu Han, Changxin Gao, Nong Sang
• Abstract summary: We present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within a cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. We also propose two strategies to improve the contrastive learning process.
• Score: 44.15222928084943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intradomain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks.

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