{"type":"ScholarlyArticle","title":"Unsupervised Vehicle Re-identification with Progressive Adaptation","authors":[{"name":"Jinjia Peng","slug":"jinjia-peng","given":"Jinjia","family":"Peng","affiliation":"College of Information and Science Technology , Dalian Maritime University , Liaoning , Dalian; Pengcheng Laboratory , Shenzhen , Guangdong"},{"name":"Yang Wang","slug":"yang-wang","given":"Yang","family":"Wang","affiliation":"Key Laboratory of Knowledge Engineering with Big Data , Ministry of education , Hefei University of Technology , China; School of Computer Science and Information Engineering , Hefei University of Technology , China"},{"name":"Huibing Wang","slug":"huibing-wang","given":"Huibing","family":"Wang","affiliation":"College of Information and Science Technology , Dalian Maritime University , Liaoning , Dalian; Pengcheng Laboratory , Shenzhen , Guangdong"},{"name":"Zhao Zhang","slug":"zhao-zhang","given":"Zhao","family":"Zhang","affiliation":"Key Laboratory of Knowledge Engineering with Big Data , Ministry of education , Hefei University of Technology , China; School of Computer Science and Information Engineering , Hefei University of Technology , China"},{"name":"Xianping Fu","slug":"xianping-fu","given":"Xianping","family":"Fu","affiliation":"College of Information and Science Technology , Dalian Maritime University , Liaoning , Dalian; Pengcheng Laboratory , Shenzhen , Guangdong"},{"name":"Meng Wang","slug":"meng-wang","given":"Meng","family":"Wang","affiliation":"Key Laboratory of Knowledge Engineering with Big Data , Ministry of education , Hefei University of Technology , China; School of Computer Science and Information Engineering , Hefei University of Technology , China"}],"abstract":"Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes; worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel Progressive Adaptation Learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as “pseudo target samples”. These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset.","doi":"10.24963/ijcai.2020/127","url":"https://ijcai.org/proceedings/2020/127","pdf":"https://ijcai.org/proceedings/2020/0127.pdf","markdown":"https://ijcai.org/proceedings/2020/127.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/127","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Computer Vision","track":"Main track","number":127,"pages":{"first":913,"last":919},"keywords":["Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Machine Learning Applications: Applications of Unsupervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25496/file_25496.m3u8","length_sec":293,"provider":"hls"}],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 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