… Vision Transformer (as in Figs. 1 and 5), we aim to improve the robustness of the Transformer
… information intact in Transformer is the unique form of token sequences processed by the …

… When pre-trained at sufficient scale, transformers achieve excellent results on tasks with
fewer datapoints. For example, when pre-trained on the JFT-300M dataset, ViT approached or …

… variants of vision transformers. (1) The residual layers of vision transformers, which are …
(2) Fine-tuning the weights of the attention layers is sufficient to adapt vision transformers to …

… issue and effectively scale the vision transformer to be deeper, … very deep vision transformers
with even 32 transformer blocks … is all lower than 30% and they present sufficient diversity. …

… Our results suggest that with sufficient data, training a larger model for fewer steps is preferable.
This observation mirrors results in language modelling and machine translation [21, 25]. …

… language and vision domains. … Transformer (Transformer-LS), an efficient self-attention
mechanism for modeling long sequences with linear complexity for both language and vision …

… of vision tasks using Transformer networks. This survey aims to provide a comprehensive
overview of the Transformer models in the computer vision … the success of Transformers, ie, self-…

… /tokens and employed a pure transformer structure. With sufficient training data, ViT outperforms
… Beyond classification, Transformer has been adopted in diverse vision tasks, including …

… plain, non-hierarchical Vision Transformer (ViT) as a … sufficient to build a simple feature
pyramid from a single-scale feature map (without the common FPN design) and (ii) it is sufficient …

… of performance on other vision-and-language downstream … embedders may not be sufficient
to learn complex vision-and-… Figure 2c use a deep transformer to model the interaction of …