Title: Saliency-guided Vision Transformer for Few-shot Keypoint Detection. Abstract: One attractive property of Vision Transformer (ViT) is to capture long-range dependency among image patches, which helps improve few-shot keypoint detection (FSKD) and is not explored yet in the literature. However, the simple application of ViT brings in irrelevant features outside of the region of interest due to the global attention matrix, thus degrading similarity learning between support and query features. In this paper, we present a novel saliency-guided vision transformer, dubbed \emph{SalViT}, for few-shot keypoint detection. Our SalViT enjoys a uniquely designed masked self-attention and a morphology learner, where the former introduces saliency map as a soft mask into ViT and constrains the self-attention to foregrounds, while the latter leverages the so-called power normalization adjusting morphology of saliency map, realizing dynamically changing receptive field. With the SalViT, we explore the use of ViT features together with CNN features to model both local and long-range dependency, providing more informative representations for similarity learning. We apply SalViT to FSKD in inductive and transductive settings, and show that it outperforms other methods. 