Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration

• URL: http://arxiv.org/abs/2506.05709v1
• Date: Fri, 06 Jun 2025 03:18:11 GMT
• Title: Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration
• Authors: Fanhu Zeng, Deli Yu, Zhenglun Kong, Hao Tang,
• Abstract summary: We propose a many-to-many Token Transforming framework that serves as a generalization of all existing methods.<n>Specifically, we reduce 40% FLOPs and accelerate DeiT-S by $times$1.5 with marginal 0.1% accuracy drop.<n>We extend the method to dense prediction tasks including segmentation, object detection, depth estimation, and language model generation.
• Score: 8.584066042703972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision transformers have been widely explored in various vision tasks. Due to heavy computational cost, much interest has aroused for compressing vision transformer dynamically in the aspect of tokens. Current methods mainly pay attention to token pruning or merging to reduce token numbers, in which tokens are compressed exclusively, causing great information loss and therefore post-training is inevitably required to recover the performance. In this paper, we rethink token reduction and unify the process as an explicit form of token matrix transformation, in which all existing methods are constructing special forms of matrices within the framework. Furthermore, we propose a many-to-many Token Transforming framework that serves as a generalization of all existing methods and reserves the most information, even enabling training-free acceleration. We conduct extensive experiments to validate our framework. Specifically, we reduce 40% FLOPs and accelerate DeiT-S by $\times$1.5 with marginal 0.1% accuracy drop. Furthermore, we extend the method to dense prediction tasks including segmentation, object detection, depth estimation, and language model generation. Results demonstrate that the proposed method consistently achieves substantial improvements, offering a better computation-performance trade-off, impressive budget reduction and inference acceleration.

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