  Person re-identification (Re-ID) is a challenging task as persons are often
in different backgrounds. Most recent Re-ID methods treat the foreground and
background information equally for person discriminative learning, but can
easily lead to potential false alarm problems when different persons are in
similar backgrounds or the same person is in different backgrounds. In this
paper, we propose a Foreground-Guided Texture-Focused Network (FTN) for Re-ID,
which can weaken the representation of unrelated background and highlight the
attributes person-related in an end-to-end manner. FTN consists of a semantic
encoder (S-Enc) and a compact foreground attention module (CFA) for Re-ID task,
and a texture-focused decoder (TF-Dec) for reconstruction task. Particularly,
we build a foreground-guided semi-supervised learning strategy for TF-Dec
because the reconstructed ground-truths are only the inputs of FTN weighted by
the Gaussian mask and the attention mask generated by CFA. Moreover, a new
gradient loss is introduced to encourage the network to mine the texture
consistency between the inputs and the reconstructed outputs. Our FTN is
computationally efficient and extensive experiments on three commonly used
datasets Market1501, CUHK03 and MSMT17 demonstrate that the proposed method
performs favorably against the state-of-the-art methods.
