{"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/solar-second-order-loss-and-attention-for","title":"SOLAR: Second-Order Loss and Attention for Image Retrieval","arxiv_id":"2001.08972","date":"2020-01-24","proceeding":"ECCV 2020 8","authors":["Tony Ng","Vassileios Balntas","Yurun Tian","Krystian Mikolajczyk"],"abstract":"Recent works in deep-learning have shown that second-order information is beneficial in many computer-vision tasks. Second-order information can be enforced both in the spatial context and the abstract feature dimensions. In this work, we explore two second-order components. 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