{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cvt-introducing-convolutions-to-vision","title":"CvT: Introducing Convolutions to Vision Transformers","arxiv_id":"2103.15808","date":"2021-03-29","proceeding":"ICCV 2021 10","authors":["Haiping Wu","Bin Xiao","Noel Codella","Mengchen Liu","Xiyang Dai","Lu Yuan","Lei Zhang"],"abstract":"We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. 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