{"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/2103-14899","title":"CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification","arxiv_id":"2103.14899","date":"2021-03-27","proceeding":"ICCV 2021 10","authors":["Chun-Fu Chen","Quanfu Fan","Rameswar Panda"],"abstract":"The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. Inspired by this, in this paper, we study how to learn multi-scale feature representations in transformer models for image classification. 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