{"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/beyond-triplet-loss-a-deep-quadruplet-network","title":"Beyond triplet loss: a deep quadruplet network for person re-identification","arxiv_id":"1704.01719","date":"2017-04-06","proceeding":"CVPR 2017 7","authors":["Weihua Chen","Xiaotang Chen","Jian-Guo Zhang","Kaiqi Huang"],"abstract":"Person re-identification (ReID) is an important task in wide area video\nsurveillance which focuses on identifying people across different cameras.\nRecently, deep learning networks with a triplet loss become a common framework\nfor person ReID. However, the triplet loss pays main attentions on obtaining\ncorrect orders on the training set. It still suffers from a weaker\ngeneralization capability from the training set to the testing set, thus\nresulting in inferior performance. In this paper, we design a quadruplet loss,\nwhich can lead to the model output with a larger inter-class variation and a\nsmaller intra-class variation compared to the triplet loss. As a result, our\nmodel has a better generalization ability and can achieve a higher performance\non the testing set. In particular, a quadruplet deep network using a\nmargin-based online hard negative mining is proposed based on the quadruplet\nloss for the person ReID. In extensive experiments, the proposed network\noutperforms most of the state-of-the-art algorithms on representative datasets\nwhich clearly demonstrates the effectiveness of our proposed method.","url_abs":"http://arxiv.org/abs/1704.01719v1","url_pdf":"http://arxiv.org/pdf/1704.01719v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"beyond-triplet-loss-a-deep-quadruplet-network","repo_url":"https://github.com/Mind23-2/MindCode-70","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":null},{"paper_slug":"beyond-triplet-loss-a-deep-quadruplet-network","repo_url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/metric_learn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"beyond-triplet-loss-a-deep-quadruplet-network","repo_url":"https://github.com/mindspore-ai/models/blob/master/research/cv/metric_learn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":null,"task_name":"Triplet"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1704.01719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.01719"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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