{"type":"ScholarlyArticle","title":"Attention-guided Contrastive Hashing for Long-tailed Image Retrieval","authors":[{"name":"Xuan Kou","slug":"xuan-kou","given":"Xuan","family":"Kou","affiliation":"Xidian University"},{"name":"Chenghao Xu","slug":"chenghao-xu","given":"Chenghao","family":"Xu","affiliation":"Xidian University"},{"name":"Xu Yang","slug":"xu-yang","given":"Xu","family":"Yang","affiliation":"Xidian University"},{"name":"Cheng Deng","slug":"cheng-deng","given":"Cheng","family":"Deng","affiliation":"Xidian University"}],"abstract":"Image hashing is to represent an image using a binary code for efficient storage and accurate retrieval. Recently, deep hashing methods have shown great improvements on ideally balanced datasets, however, long-tailed data is more common due to rare samples or data collection costs in the real world. Toward that end, this paper introduces a simple yet effective model named Attention-guided Contrastive Hashing Network (ACHNet) for long-tailed hashing. Specifically, a cross attention feature enhancement module is proposed to predict the importance of features for hashing, alleviating the loss of information originated from data dimension reduction. Moreover, unlike recently sota contrastive methods that focus on instance-level discrimination, we optimize an innovative category-centered contrastive hashing to obtain discriminative results, which is more suitable for long-tailed scenarios. Experiments on two popular benchmarks verify the superiority of the proposed method.  Our code is available at: https://github.com/KUXN98/ACHNet.","doi":"10.24963/ijcai.2022/142","url":"https://ijcai.org/proceedings/2022/142","pdf":"https://ijcai.org/proceedings/2022/0142.pdf","markdown":"https://ijcai.org/proceedings/2022/142.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/142","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Computer Vision","track":"Main Track","number":142,"pages":{"first":1017,"last":1023},"keywords":["Computer Vision: Representation Learning","Computer Vision: Recognition (object detection, categorization)","Computer Vision: Image and Video retrieval"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986535","length_sec":87,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984678","length_sec":309,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Cai et al., 2021] Jiarui Cai, Yizhou Wang, and Jenq-Neng Hwang. 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