Multiple Expert Brainstorming for Domain Adaptive Person Re-identification

• URL: http://arxiv.org/abs/2007.01546v3
• Date: Mon, 13 Jul 2020 13:11:44 GMT
• Title: Multiple Expert Brainstorming for Domain Adaptive Person Re-identification
• Authors: Yunpeng Zhai, Qixiang Ye, Shijian Lu, Mengxi Jia, Rongrong Ji and Yonghong Tian
• Abstract summary: We propose a multiple expert brainstorming network (MEB-Net) for domain adaptive person re-ID. MEB-Net adopts a mutual learning strategy, where multiple networks with different architectures are pre-trained within a source domain. Experiments on large-scale datasets demonstrate the superior performance of MEB-Net over the state-of-the-arts.
• Score: 140.3998019639158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains unexplored. In this paper, we propose a multiple expert brainstorming network (MEB-Net) for domain adaptive person re-ID, opening up a promising direction about model ensemble problem under unsupervised conditions. MEB-Net adopts a mutual learning strategy, where multiple networks with different architectures are pre-trained within a source domain as expert models equipped with specific features and knowledge, while the adaptation is then accomplished through brainstorming (mutual learning) among expert models. MEB-Net accommodates the heterogeneity of experts learned with different architectures and enhances discrimination capability of the adapted re-ID model, by introducing a regularization scheme about authority of experts. Extensive experiments on large-scale datasets (Market-1501 and DukeMTMC-reID) demonstrate the superior performance of MEB-Net over the state-of-the-arts.

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