Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words

arXiv:2309.16108v4 Announce Type: replace-cross
Abstract: Vision Transformer (ViT) has emerged as a powerful architecture in the realm of modern computer vision. However, its application in certain imaging fields, such as microscopy and satellite imaging, presents unique challenges. In these domains, images often contain multiple channels, each carrying semantically distinct and independent information. Furthermore, the model must demonstrate robustness to sparsity in input channels, as they may not be densely available during training or testing. In this paper, we propose a …

arxiv cs.ai cs.cv cs.lg image transformers type vision vision transformers words