{"type":"ScholarlyArticle","title":"Boosted Zero-Shot Learning with Semantic Correlation Regularization","authors":[{"name":"Te Pi","slug":"te-pi","given":"Te","family":"Pi","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Xi Li","slug":"xi-li","given":"Xi","family":"Li","affiliation":"Zhejiang University , Hangzhou , China; Alibaba-Zhejiang University Joint Institute of Frontier Technologies , Hangzhou , China"},{"name":"Zhongfei (Mark) Zhang","slug":"zhongfei-mark-zhang","given":"Zhongfei (Mark)","family":"Zhang","affiliation":"Zhejiang University , Hangzhou , China"}],"abstract":"We study zero-shot learning (ZSL) as a transfer learning problem, and focus on the two key aspects of ZSL, model effectiveness and model adaptation. For effective modeling, we adopt the boosting strategy to learn a zero-shot classifier from weak models to a strong model. For adaptable knowledge transfer, we devise a Semantic Correlation Regularization (SCR) approach to regularize the boosted model to be consistent with the inter-class semantic correlations. With SCR embedded in the boosting objective, and with a self-controlled sample selection for learning robustness, we propose a unified framework, Boosted Zero-shot classification with Semantic Correlation Regularization (BZ-SCR). By balancing the SCR-regularized boosted model selection and the self-controlled sample selection, BZ-SCR is capable of capturing both discriminative and adaptable feature-to-class semantic alignments, while ensuring the reliability and adaptability of the learned samples. The experiments on two ZSL datasets show the superiority of BZ-SCR over the state-of-the-arts.","doi":"10.24963/ijcai.2017/362","url":"https://ijcai.org/proceedings/2017/362","pdf":"https://ijcai.org/proceedings/2017/0362.pdf","markdown":"https://ijcai.org/proceedings/2017/362.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/362","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":362,"pages":{"first":2599,"last":2605},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Ensemble Methods","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2013] Zeynep Akata, Florent Perronnin, Zaïd Harchaoui, and Cordelia Schmid. 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