  Pedestrian attribute recognition (PAR) has received increasing attention
because of its wide application in video surveillance and pedestrian analysis.
Extracting robust feature representation is one of the key challenges in this
task. The existing methods mainly use the convolutional neural network (CNN) as
the backbone network to extract features. However, these methods mainly focus
on small discriminative regions while ignoring the global perspective. To
overcome these limitations, we propose a pure transformer-based multi-task PAR
network named PARFormer, which includes four modules. In the feature extraction
module, we build a transformer-based strong baseline for feature extraction,
which achieves competitive results on several PAR benchmarks compared with the
existing CNN-based baseline methods. In the feature processing module, we
propose an effective data augmentation strategy named batch random mask (BRM)
block to reinforce the attentive feature learning of random patches.
Furthermore, we propose a multi-attribute center loss (MACL) to enhance the
inter-attribute discriminability in the feature representations. In the
viewpoint perception module, we explore the impact of viewpoints on pedestrian
attributes, and propose a multi-view contrastive loss (MCVL) that enables the
network to exploit the viewpoint information. In the attribute recognition
module, we alleviate the negative-positive imbalance problem to generate the
attribute predictions. The above modules interact and jointly learn a highly
discriminative feature space, and supervise the generation of the final
features. Extensive experimental results show that the proposed PARFormer
network performs well compared to the state-of-the-art methods on several
public datasets, including PETA, RAP, and PA100K. Code will be released at
https://github.com/xwf199/PARFormer.
