Summary of An Empirical Study Of Mamba-based Pedestrian Attribute Recognition, by Xiao Wang et al.
An Empirical Study of Mamba-based Pedestrian Attribute Recognition
by Xiao Wang, Weizhe Kong, Jiandong Jin, Shiao Wang, Ruichong Gao, Qingchuan Ma, Chenglong Li, Jin Tang
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the applicability of Mamba, a linear complexity model, for pedestrian attribute recognition (PAR) tasks. Building upon recent advancements in Transformer networks, the authors adapt Mamba into two typical PAR frameworks: text-image fusion and pure vision multi-label recognition. Experimental results show that enhancing Mamba with a Transformer does not always lead to performance improvements but yields better results under certain settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to use Mamba, a new kind of computer model, for recognizing attributes about people in pictures or videos. The researchers tried combining Mamba with another type of model called Transformer to see if it would make their predictions more accurate. They found that sometimes this combination works well, but other times it doesn’t. This study can help inspire further research into using Mamba for recognizing people’s attributes and might even be useful in other areas like multi-label recognition. |
Keywords
» Artificial intelligence » Transformer
