  The vision transformer is a model that breaks down each image into a sequence
of tokens with a fixed length and processes them similarly to words in natural
language processing. Although increasing the number of tokens typically results
in better performance, it also leads to a considerable increase in
computational cost. Motivated by the saying "A picture is worth a thousand
words," we propose an innovative approach to accelerate the ViT model by
shortening long images. Specifically, we introduce a method for adaptively
assigning token length for each image at test time to accelerate inference
speed. First, we train a Resizable-ViT (ReViT) model capable of processing
input with diverse token lengths. Next, we extract token-length labels from
ReViT that indicate the minimum number of tokens required to achieve accurate
predictions. We then use these labels to train a lightweight Token-Length
Assigner (TLA) that allocates the optimal token length for each image during
inference. The TLA enables ReViT to process images with the minimum sufficient
number of tokens, reducing token numbers in the ViT model and improving
inference speed. Our approach is general and compatible with modern vision
transformer architectures, significantly reducing computational costs. We
verified the effectiveness of our methods on multiple representative ViT models
on image classification and action recognition.
