Vision Transformer has shown great visual representation power in substantial vision tasks such as recognition and detection, and thus been attracting fast-growing efforts on manually designing more effective architectures. In this paper, we propose to use neural architecture search to automate this process, by searching not only the architecture but also the search space. The central idea is to gradually evolve different search dimensions guided by their E-T Error computed using a weight-sharing supernet. Moreover, we provide design guidelines of general vision transformers with extensive analysis according to the space searching process, which could promote the understanding of vision transformer. Remarkably, the searched models, named S3 (short for Searching the Search Space), from the searched space achieve superior performance to recently proposed models, such as Swin, DeiT and ViT, when evaluated on ImageNet. The effectiveness of S3 is also illustrated on object detection, semantic segmentation and visual question answering, demonstrating its generality to downstream vision and vision-language tasks. Code and models will be available at https://github.com/microsoft/Cream.

本文介绍了使用神经架构搜索来自动化提高Vision Transformer架构和搜索空间的过程，以及设计指南和分析。通过使用E-T Error来逐步演化不同的搜索维度。此外，从所搜索的空间中得到的模型，名为S3，评估在ImageNet上表现比最近提出的模型（如Swin、DeiT和ViT）更出色，并在目标检测、语义分割和视觉问答等任务中展示了其普适性。

搜索视觉Transformer的搜索空间