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Searching the Search Space of Vision Transformer
Minghao Chen · Kan Wu · Bolin Ni · Houwen Peng · Bei Liu · Jianlong Fu · Hongyang Chao · Haibin Ling

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

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.

Author Information

Minghao Chen (Stony Brook University)
Kan Wu (Sun Yat-sen University)
Bolin Ni (Institute of automation, Chinese academy of science, Chinese Academy of Sciences)
Houwen Peng (Microsoft Research)
Bei Liu (Microsoft Research Asia)
Jianlong Fu (Microsoft Research)
Hongyang Chao
Haibin Ling (State University of New York, Stony Brook)

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