Title: Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Mamba, Token Efficiency

Abstract: Vision Mamba (e.g., Vim) has successfully been integrated into computer vision, and token reduction has yielded promising outcomes in Vision Transformers (ViTs). However, token reduction performs less effectively on Vision Mamba compared to ViTs. Pruning informative tokens in Mamba leads to a high loss of key knowledge tokens and a drop in performance, making it not a good solution for enhancing efficiency. Token merging, which preserves more token information than pruning, has demonstrated commendable performance in ViTs, but vanilla merging performance decreases as the reduction ratio increases either, failing to maintain the key knowledge and performance in Mamba. Re-training the model with token merging, which effectively rebuilds the key knowledge, enhances the performance of Mamba. Empirically, pruned Vims, recovered on ImageNet-1K, only drop up to 0.9% accuracy, by our proposed framework R-MeeTo in our main evaluation. We show how simple and effective the fast recovery can be achieved at minute-level, in particular, a 35.9% accuracy spike over 3 epochs of training on Vim-Ti. Moreover, Vim-Ti/S/B are re-trained within 5/7/17 minutes, and Vim-S only drop 1.3% with 1.2 $\times$ (up to 1.5 $\times$) speed up in inference.

TL;DR: Mamba models have different token pruning robustness. Marginal enrichment phenomenon is the key difference. Mamba's SSMs modules keep information in marginal tokens (end of scanning).