Accelerating Vision Transformers with Adaptive Patch Sizes
Authors: name: Rohan Choudhury, name: JungEun Kim, name: Jinhyung Park, name: Eunho Yang, name: László A. Jeni, name: Kris M. Kitani
Organization: Carnegie Mellon University School of Computer Science

Vision Transformers (ViTs) partition input images into uniformly sized
patches regardless of their content, resulting in long input sequence lengths
for high-resolution images. We present Adaptive Patch Transformers (APT), which
addresses this by using multiple different patch sizes within the same image.
APT reduces the total number of input tokens by allocating larger patch sizes
in more homogeneous areas and smaller patches in more complex ones. APT
achieves a drastic speedup in ViT inference and training, increasing throughput
by 40% on ViT-L and 50% on ViT-H while maintaining downstream performance, and
can be applied to a previously fine-tuned ViT, converging in as little as 1
epoch. It also significantly reduces training and inference time without loss
of performance in high-resolution dense visual tasks, achieving up to 30\%
faster training and inference in visual QA, object detection, and semantic
segmentation.