{"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/vmamba-visual-state-space-model","title":"VMamba: Visual State Space Model","arxiv_id":"2401.10166","date":"2024-01-18","proceeding":null,"authors":["Yue Liu","Yunjie Tian","Yuzhong Zhao","Hongtian Yu","Lingxi Xie","YaoWei Wang","Qixiang Ye","Jianbin Jiao","Yunfan Liu"],"abstract":"Designing computationally efficient network architectures remains an ongoing necessity in computer vision. In this paper, we adapt Mamba, a state-space language model, into VMamba, a vision backbone with linear time complexity. 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