Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2008.02520v1
• Date: Thu, 6 Aug 2020 08:43:35 GMT
• Title: Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification
• Authors: Nan Pu, Wei Chen, Yu Liu, Erwin M. Bakker, Michael S. Lew
• Abstract summary: Visible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. We present a dual Gaussian-based variational auto-encoder (DG-VAE) to disentangle an identity-discriminable and an identity-ambiguous cross-modality feature subspace. Our method outperforms state-of-the-art methods on two VI-ReID datasets.
• Score: 19.481092102536827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. Except for the intra-modality variance that RGB-RGB person re-identification mainly overcomes, VI-ReID suffers from additional inter-modality variance caused by the inherent heterogeneous gap. To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively. Disentangling cross-modality identity-discriminable features leads to more robust retrieval for VI-ReID. To achieve efficient optimization like conventional VAE, we theoretically derive two variational inference terms for the MoG prior under the supervised setting, which not only restricts the identity-discriminable subspace so that the model explicitly handles the cross-modality intra-identity variance, but also enables the MoG distribution to avoid posterior collapse. Furthermore, we propose a triplet swap reconstruction (TSR) strategy to promote the above disentangling process. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two VI-ReID datasets.

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