  Vision transformers have achieved significant improvements on various vision
tasks but their quadratic interactions between tokens significantly reduce
computational efficiency. Many pruning methods have been proposed to remove
redundant tokens for efficient vision transformers recently. However, existing
studies mainly focus on the token importance to preserve local attentive tokens
but completely ignore the global token diversity. In this paper, we emphasize
the cruciality of diverse global semantics and propose an efficient token
decoupling and merging method that can jointly consider the token importance
and diversity for token pruning. According to the class token attention, we
decouple the attentive and inattentive tokens. In addition to preserving the
most discriminative local tokens, we merge similar inattentive tokens and match
homogeneous attentive tokens to maximize the token diversity. Despite its
simplicity, our method obtains a promising trade-off between model complexity
and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35%
with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token
diversity, our method can even improve the accuracy of DeiT-T by 0.1% after
reducing its FLOPs by 40%.
