  Text-based person re-identification(Re-id) is an important task in video
surveillance, which consists of retrieving the corresponding person's image
given a textual description from a large gallery of images. It is difficult to
directly match visual contents with the textual descriptions due to the
modality heterogeneity. On the one hand, the textual embeddings are not
discriminative enough, which originates from the high abstraction of the
textual descriptions. One the other hand,Global average pooling (GAP) is
commonly utilized to extract more general or smoothed features implicitly but
ignores salient local features, which are more important for the cross-modal
matching problem. With that in mind, a novel Dual-path CNN with Max Gated block
(DCMG) is proposed to extract discriminative word embeddings and make
visual-textual association concern more on remarkable features of both
modalities. The proposed framework is based on two deep residual CNNs jointly
optimized with cross-modal projection matching (CMPM) loss and cross-modal
projection classification (CMPC) loss to embed the two modalities into a joint
feature space. First, the pre-trained language model, BERT, is combined with
the convolutional neural network (CNN) to learn better word embeddings in the
text-to-image matching domain. Second, the global Max pooling (GMP) layer is
applied to make the visual-textual features focus more on the salient part. To
further alleviate the noise of the maxed-pooled features, the gated block (GB)
is proposed to produce an attention map that focuses on meaningful features of
both modalities. Finally, extensive experiments are conducted on the benchmark
dataset, CUHK-PEDES, in which our approach achieves the rank-1 score of 55.81%
and outperforms the state-of-the-art method by 1.3%.
