{"type":"ScholarlyArticle","title":"VS-Boost: Boosting Visual-Semantic Association for Generalized Zero-Shot Learning","authors":[{"name":"Xiaofan Li","slug":"xiaofan-li","given":"Xiaofan","family":"Li","affiliation":"School of Informatics , Xiamen University , Fujian , China"},{"name":"Yachao Zhang","slug":"yachao-zhang","given":"Yachao","family":"Zhang","affiliation":"Tsinghua University , Shenzhen , China"},{"name":"Shiran Bian","slug":"shiran-bian","given":"Shiran","family":"Bian","affiliation":"School of Informatics , Xiamen University , Fujian , China"},{"name":"Yanyun Qu","slug":"yanyun-qu","given":"Yanyun","family":"Qu","affiliation":"School of Informatics , Xiamen University , Fujian , China"},{"name":"Yuan Xie","slug":"yuan-xie","given":"Yuan","family":"Xie","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai , China"},{"name":"Zhongchao Shi","slug":"zhongchao-shi","given":"Zhongchao","family":"Shi","affiliation":"Lenovo Research , Beijing , China"},{"name":"Jianping Fan","slug":"jianping-fan","given":"Jianping","family":"Fan","affiliation":"Lenovo Research , Beijing , China"}],"abstract":"Unlike conventional zero-shot learning (CZSL) which only focuses on the recognition of unseen classes by using the classifier trained on seen classes and semantic embeddings, generalized zero-shot learning (GZSL) aims at recognizing both the seen and unseen classes, so it is more challenging due to the extreme training imbalance. Recently, some feature generation methods introduce metric learning to enhance the discriminability of visual features. Although these methods achieve good results, they focus only on metric learning in the visual feature space to enhance features and ignore the association between the feature space and the semantic space. Since the GZSL method uses semantics as prior knowledge to migrate visual knowledge to unseen classes, the consistency between visual space and semantic space is critical. To this end, we propose relational metric learning which can relate the metrics in the two spaces and make the distribution of the two spaces more consistent. Based on the generation method and relational metric learning, we proposed a novel GZSL method, termed VS-Boost, which can effectively boost the association between vision and semantics. The experimental results demonstrate that our method is effective and achieves significant gains on five benchmark datasets compared with the state-of-the-art methods.","doi":"10.24963/ijcai.2023/123","url":"https://ijcai.org/proceedings/2023/123","pdf":"https://ijcai.org/proceedings/2023/0123.pdf","markdown":"https://ijcai.org/proceedings/2023/123.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/123","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":123,"pages":{"first":1107,"last":1115},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Neural generative models, auto encoders, GANs"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2015a] Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid. 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