{"type":"ScholarlyArticle","title":"Incremental Few-Shot Learning for Pedestrian Attribute Recognition","authors":[{"name":"Liuyu Xiang","slug":"liuyu-xiang","given":"Liuyu","family":"Xiang","affiliation":"School of Software , Tsinghua University , Beijing , China"},{"name":"Xiaoming Jin","slug":"xiaoming-jin","given":"Xiaoming","family":"Jin","affiliation":"School of Software , Tsinghua University , Beijing , China"},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":"School of Software , Tsinghua University , Beijing , China"},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":"WMG Data Science , University of Warwick , CV4 7AL Coventry , United Kingdom"},{"name":"Leida Li","slug":"leida-li","given":"Leida","family":"Li","affiliation":"School of Artificial Intelligence , Xidian University , Xi'an 710071 , China"}],"abstract":"Pedestrian attribute recognition has received\r\nincreasing attention due to its important role\r\nin video surveillance applications. However,\r\nmost existing methods are designed for a fixed\r\nset of attributes. They are unable to handle\r\nthe incremental few-shot learning scenario, i.e.\r\nadapting a well-trained model to newly added\r\nattributes with scarce data, which commonly\r\nexists in the real world. In this work, we\r\npresent a meta learning based method to address\r\nthis issue. The core of our framework\r\nis a meta architecture capable of disentangling\r\nmultiple attribute information and generalizing\r\nrapidly to new coming attributes. By conducting\r\nextensive experiments on the benchmark\r\ndataset PETA and RAP under the incremental\r\nfew-shot setting, we show that our method is\r\nable to perform the task with competitive performances\r\nand low resource requirements.","doi":"10.24963/ijcai.2019/543","url":"https://ijcai.org/proceedings/2019/543","pdf":"https://ijcai.org/proceedings/2019/0543.pdf","markdown":"https://ijcai.org/proceedings/2019/543.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/543","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":543,"pages":{"first":3912,"last":3918},"keywords":["Machine Learning: Deep Learning","Machine Learning Applications: Applications of Supervised Learning","Computer Vision: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Bertinetto et al., 2016] Luca Bertinetto, João F Henriques, Jack Valmadre, Philip Torr, and Andrea Vedaldi. 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