{"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/bottleneck-transformers-for-visual","title":"Bottleneck Transformers for Visual Recognition","arxiv_id":"2101.11605","date":"2021-01-27","proceeding":"CVPR 2021 1","authors":["Aravind Srinivas","Tsung-Yi Lin","Niki Parmar","Jonathon Shlens","Pieter Abbeel","Ashish Vaswani"],"abstract":"We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. 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