| We are happy to announce that a paper by LML member, Nan Pu, has been accepted at the top multimedia conference in the world, ACM Multimedia.
Dual Gaussian-based Variational Subspace Disentanglement for
Visible-Infrared Person Re-Identification
Visible-infrared person re-identifi cation (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-
identifi cation mainly overcomes, VI-ReID suff ers 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. Disentan-
gling cross-modality identity-discriminable features leads to more
robust retrieval for VI-ReID. To achieve effi cient 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. Fur-
thermore, 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.