Improving robustness for vision transformer with a simple dynamic scanning augmentation

Author:

Kotyan ShashankORCID,Vargas Danilo VasconcellosORCID

Funder

Japan Science and Technology Agency

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference41 articles.

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2. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, in: International Conference on Learning Representations, 2020.

3. Stand-alone self-attention in vision models;Ramachandran,2019

4. Visual transformers: Token-based image representation and processing for computer vision;Wu,2020

5. Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10012–10022.

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