Summary of Res-vmamba: Fine-grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning, by Chi-sheng Chen et al.
Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning
by Chi-Sheng Chen, Guan-Ying Chen, Dong Zhou, Di Jiang, Dai-Shi Chen
First submitted to arxiv on: 24 Feb 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 proposed Res-VMamba model integrates a residual learning framework within the VMamba architecture to concurrently harness global and local state features. This enhances fine-grained classification performance, surpassing current state-of-the-art models on the CNFOOD-241 dataset with an accuracy of 79.54% without pre-trained weights. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Res-VMamba model is designed for food recognition tasks, building upon the VMamba architecture that demonstrated superior performance and computation efficiency compared to Transformer architectures. The integration of a residual learning framework allows for the effective utilization of both global and local state features inherent in the original VMamba design. |
Keywords
» Artificial intelligence » Classification » Transformer
