Summary of Faster Vision Mamba Is Rebuilt in Minutes Via Merged Token Re-training, by Mingjia Shi et al.
Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training
by Mingjia Shi, Yuhao Zhou, Ruiji Yu, Zekai Li, Zhiyuan Liang, Xuanlei Zhao, Xiaojiang Peng, Shanmukha Ramakrishna Vedantam, Wangbo Zhao, Kai Wang, Yang You
First submitted to arxiv on: 17 Dec 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 efficiency of Vision Mamba, a computer vision model that has achieved near-state-of-the-art performance on various tasks. The authors explore token reduction as a promising approach to increase Mamba’s efficiency, building upon successful implementations in ViTs. They find that pruning informative tokens leads to significant losses in key knowledge and degraded performance. Instead, they propose merging tokens, which preserves more information but still suffers from large compression ratios. To mitigate this issue, the authors suggest a quick retraining round after token merging, demonstrating robust results across various compression ratios. The proposed framework, R-MeeTo, achieves impressive accuracy spikes of up to 35.9% in just three epochs of training on Vim-Ti. Furthermore, the authors show that re-training is possible within minutes, with some models experiencing only a 1.3% drop in accuracy and a 1.2x to 1.5x speedup in inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make Vision Mamba, a computer vision model, work more efficiently without losing its abilities. They try different methods to reduce the amount of information it uses, but find that some of these methods can cause significant losses. Instead, they suggest merging the remaining important information together. To fix this issue, they propose re-training the model after merging the tokens, which helps maintain the original performance. Their method, R-MeeTo, makes the model faster and more accurate, with some improvements being as high as 35.9%. Additionally, their approach allows for quick re-training, taking only a few minutes. |
Keywords
» Artificial intelligence » Inference » Pruning » Token
