  Current strong pedestrian attribute recognition models are developed based on
Transformer networks, which are computationally heavy. Recently proposed models
with linear complexity (e.g., Mamba) have garnered significant attention and
have achieved a good balance between accuracy and computational cost across a
variety of visual tasks. Relevant review articles also suggest that while these
models can perform well on some pedestrian attribute recognition datasets, they
are generally weaker than the corresponding Transformer models. To further tap
into the potential of the novel Mamba architecture for PAR tasks, this paper
designs and adapts Mamba into two typical PAR frameworks, i.e., the text-image
fusion approach and pure vision Mamba multi-label recognition framework. It is
found that interacting with attribute tags as additional input does not always
lead to an improvement, specifically, Vim can be enhanced, but VMamba cannot.
This paper further designs various hybrid Mamba-Transformer variants and
conducts thorough experimental validations. These experimental results indicate
that simply enhancing Mamba with a Transformer does not always lead to
performance improvements but yields better results under certain settings. We
hope this empirical study can further inspire research in Mamba for PAR, and
even extend into the domain of multi-label recognition, through the design of
these network structures and comprehensive experimentation. The source code of
this work will be released at \url{https://github.com/Event-AHU/OpenPAR}
