PPT: Token Pruning and Pooling for Efficient Vision Transformers

Xinjian Wu, Fanhu Zeng, Xiudong Wang, Yunhe Wang, Xinghao Chen

Keywords: Vision Transformers, Token Pruning, Token Pooling, Model Acceleration

TL;DR: We heuristically propose an instance-aware strategy to adaptively select the optimal policy of token pruning or token pooling in different layers of ViTs. Our method, PPT, effectively reduces model complexity while preserving predictive accuracy.

Abstract: Vision Transformers (ViTs) have emerged as powerful models in the field of computer vision, delivering superior performance across various vision tasks. However, the high computational complexity poses a significant barrier to their practical applications in real-world scenarios. Motivated by the fact that not all tokens contribute equally to the final predictions and fewer tokens bring less computational cost, reducing redundant tokens has become a prevailing paradigm for accelerating vision transformers. However, we argue that it is not optimal to either only reduce inattentive redundancy by token pruning, or only reduce duplicative redundancy by token merging. To this end, in this paper we propose a novel acceleration framework, namely token Pruning & Pooling Transformers (PPT), to adaptively tackle these two types of redundancy in different layers. By heuristically integrating both token pruning and token pooling techniques in ViTs without additional trainable parameters, PPT effectively reduces the model complexity while maintaining its predictive accuracy. For example, PPT reduces over 37% FLOPs and improves the throughput by over 45% for DeiT-S without any accuracy drop on the ImageNet dataset.

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Submission Number: 80