{"type":"ScholarlyArticle","title":"Dual Expert Distillation Network for Generalized Zero-Shot Learning","authors":[{"name":"Zhijie Rao","slug":"zhijie-rao","given":"Zhijie","family":"Rao","affiliation":"Department of Computing , The Hong Kong Polytechnic University , Hong Kong SAR , China"},{"name":"Jingcai Guo","slug":"jingcai-guo","given":"Jingcai","family":"Guo","affiliation":"Department of Computing , The Hong Kong Polytechnic University , Hong Kong SAR , China; Hong Kong Polytechnic University Shenzhen Research Institute , Shenzhen , China"},{"name":"Xiaocheng Lu","slug":"xiaocheng-lu","given":"Xiaocheng","family":"Lu","affiliation":"Department of Computer Science and Engineering , HKUST , Hong Kong SAR , China"},{"name":"Jingming Liang","slug":"jingming-liang","given":"Jingming","family":"Liang","affiliation":"Department of Computing , The Hong Kong Polytechnic University , Hong Kong SAR , China; Department of Computer Science , The University of Iowa , Iowa City , United States"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"Department of Computing , The Hong Kong Polytechnic University , Hong Kong SAR , China"},{"name":"Haozhao Wang","slug":"haozhao-wang","given":"Haozhao","family":"Wang","affiliation":"School of Computer Science and Technology , HUST , Wuhan , China"},{"name":"Kang Wei","slug":"kang-wei","given":"Kang","family":"Wei","affiliation":""},{"name":"Xiaofeng Cao","slug":"xiaofeng-cao","given":"Xiaofeng","family":"Cao","affiliation":"School of Artificial Intelligence , Jilin University , Changchun , China"}],"abstract":"Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation. Existing studies resort to refining a uniform mapping function to align and correlate the sample regions and subattributes, ignoring two crucial issues: 1) the inherent asymmetry of attributes; and 2) the unutilized channel information. This paper addresses these issues by introducing a simple yet effective approach, dubbed Dual Expert Distillation Network (DEDN), where two experts are dedicated to coarse- and fine-grained visual-attribute modeling, respectively. Concretely, one coarse expert, namely cExp, has a complete perceptual scope to coordinate visual-attribute similarity metrics across dimensions, and moreover, another fine expert, namely fExp, consists of multiple specialized subnetworks, each corresponds to an exclusive set of attributes. Two experts cooperatively distill from each other to reach a mutual agreement during training. Meanwhile, we further equip DEDN with a newly designed backbone network, i.e., Dual Attention Network (DAN), which incorporates both region and channel attention information to fully exploit and leverage visual semantic knowledge. Extensive experiments on various benchmark datasets indicate a new state-of-the-art. The code is available at github.com/zjrao/DEDN.","doi":"10.24963/ijcai.2024/534","url":"https://ijcai.org/proceedings/2024/534","pdf":"https://ijcai.org/proceedings/2024/0534.pdf","markdown":"https://ijcai.org/proceedings/2024/534.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/534","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":534,"pages":{"first":4833,"last":4841},"keywords":["Machine Learning: ML: Few-shot learning","Machine Learning: ML: Cost-sensitive learning"],"videos":[],"references":[{"position":1,"raw":"[Annadani and Biswas, 2018] Yashas Annadani and Soma Biswas. 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