{"type":"ScholarlyArticle","title":"Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting","authors":[{"name":"Tiange Zhang","slug":"tiange-zhang","given":"Tiange","family":"Zhang","affiliation":"Faculty of Computer Science and Technology , Ocean University of China"},{"name":"Qing Cai","slug":"qing-cai","given":"Qing","family":"Cai","affiliation":"Faculty of Computer Science and Technology , Ocean University of China"},{"name":"Feng Gao","slug":"feng-gao","given":"Feng","family":"Gao","affiliation":"Faculty of Computer Science and Technology , Ocean University of China"},{"name":"Lin Qi","slug":"lin-qi","given":"Lin","family":"Qi","affiliation":"Faculty of Computer Science and Technology , Ocean University of China"},{"name":"Junyu Dong","slug":"junyu-dong","given":"Junyu","family":"Dong","affiliation":"Faculty of Computer Science and Technology , Ocean University of China"}],"abstract":"Cross-Domain Few-Shot Learning has witnessed great stride with the development of meta-learning. However, most existing methods pay more attention to learning domain-adaptive inductive bias (meta-knowledge) through feature-wise manipulation or task diversity improvement while neglecting the phenomenon that deep networks tend to rely more on high-frequency cues to make the classification decision, which thus degenerates the robustness of learned inductive bias since high-frequency information is vulnerable and easy to be disturbed by noisy information. Hence in this paper, we make one of the first attempts to propose a Frequency-Aware Prompting method with mutual attention for Cross-Domain Few-Shot classification, which can let networks simulate the human visual perception of selecting different frequency cues when facing new recognition tasks. Specifically, a frequency-aware prompting mechanism is first proposed, in which high-frequency components of the decomposed source image are switched either with normal distribution sampling or zeroing to get frequency-aware augment samples. Then, a mutual attention module is designed to learn generalizable inductive bias under CD-FSL settings. More importantly, the proposed method is a plug-and-play module that can be directly applied to most off-the-shelf CD-FLS methods. Experimental results on CD-FSL benchmarks demonstrate the effectiveness of our proposed method as well as robustly improve the performance of existing CD-FLS methods. Resources at https://github.com/tinkez/FAP_CDFSC.","doi":"10.24963/ijcai.2024/607","url":"https://ijcai.org/proceedings/2024/607","pdf":"https://ijcai.org/proceedings/2024/0607.pdf","markdown":"https://ijcai.org/proceedings/2024/607.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/607","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":607,"pages":{"first":5490,"last":5498},"keywords":["Machine Learning: ML: Meta-learning","Machine Learning: ML: Classification","Machine Learning: ML: Multi-task and transfer learning","Machine Learning: ML: Few-shot learning"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2019] Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Wang, and Jia-Bin Huang. 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