{"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/cross-dataset-person-re-identification-using","title":"Cross-dataset Person Re-Identification Using Similarity Preserved Generative Adversarial Networks","arxiv_id":"1806.04533","date":"2018-06-11","proceeding":null,"authors":["Jianming Lv","Xintong Wang"],"abstract":"Person re-identification (Re-ID) aims to match the image frames which contain\nthe same person in the surveillance videos. Most of the Re-ID algorithms\nconduct supervised training in some small labeled datasets, so directly\ndeploying these trained models to the real-world large camera networks may lead\nto a poor performance due to underfitting. The significant difference between\nthe source training dataset and the target testing dataset makes it challenging\nto incrementally optimize the model. To address this challenge, we propose a\nnovel solution by transforming the unlabeled images in the target domain to fit\nthe original classifier by using our proposed similarity preserved generative\nadversarial networks model, SimPGAN. Specifically, SimPGAN adopts the\ngenerative adversarial networks with the cycle consistency constraint to\ntransform the unlabeled images in the target domain to the style of the source\ndomain. Meanwhile, SimPGAN uses the similarity consistency loss, which is\nmeasured by a siamese deep convolutional neural network, to preserve the\nsimilarity of the transformed images of the same person. Comprehensive\nexperiments based on multiple real surveillance datasets are conducted, and the\nresults show that our algorithm is better than the state-of-the-art\ncross-dataset unsupervised person Re-ID algorithms.","url_abs":"http://arxiv.org/abs/1806.04533v2","url_pdf":"http://arxiv.org/pdf/1806.04533v2.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":"cross-dataset-person-re-identification-using","repo_url":"https://github.com/Ethanscuter/SimPGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"person-re-identification","task_name":"Person Re-Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}