  Text-based person re-identification (ReID) aims to identify images of the
targeted person from a large-scale person image database according to a given
textual description. However, due to significant inter-modal gaps, text-based
person ReID remains a challenging problem. Most existing methods generally rely
heavily on the similarity contributed by matched word-region pairs, while
neglecting mismatched word-region pairs which may play a decisive role.
Accordingly, we propose to mine false positive examples (MFPE) via a jointly
optimized multi-branch architecture to handle this problem. MFPE contains three
branches including a false positive mining (FPM) branch to highlight the role
of mismatched word-region pairs. Besides, MFPE delicately designs a cross-relu
loss to increase the gap of similarity scores between matched and mismatched
word-region pairs. Extensive experiments on CUHK-PEDES demonstrate the superior
effectiveness of MFPE. Our code is released at
https://github.com/xx-adeline/MFPE.
