SequencePAR: Understanding Pedestrian Attributes via A Sequence
  Generation Paradigm
Jiandong Jin Xiao Wang Chenglong Li Lili Huang Jin Tang
Current pedestrian attribute recognition (PAR) algorithms are developed based
on multi-label or multi-task learning frameworks, which aim to discriminate the
attributes using specific classification heads. However, these discriminative
models are easily influenced by imbalanced data or noisy samples. Inspired by
the success of generative models, we rethink the pedestrian attribute
recognition scheme and believe the generative models may perform better on
modeling dependencies and complexity between human attributes. In this paper,
we propose a novel sequence generation paradigm for pedestrian attribute
recognition, termed SequencePAR. It extracts the pedestrian features using a
pre-trained CLIP model and embeds the attribute set into query tokens under the
guidance of text prompts. Then, a Transformer decoder is proposed to generate
the human attributes by incorporating the visual features and attribute query
tokens. The masked multi-head attention layer is introduced into the decoder
module to prevent the model from remembering the next attribute while making
attribute predictions during training. Extensive experiments on multiple widely
used pedestrian attribute recognition datasets fully validated the
effectiveness of our proposed SequencePAR. The source code and pre-trained
models will be released at https://github.com/Event-AHU/OpenPAR.