FR2ViT: Finetuning-free Token Reduction for Dense Prediction Through a Refinement-Reactivation Architecture
Token reduction is an efficient method for accelerating vision transformers. Techniques like token pruning and merging progressively decrease the number of active tokens to reduce the computation cost. However, when applied to dense prediction tasks, these techniques crudely cache low-level features…