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学者姓名：卓林海
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Abstract ：
The challenge of cross-domain few-shot learning (CD-FSL) stems from the substantial distribution disparities between target and source domain images, necessitating a model with robust generalization capabilities. In this work, we posit that large-scale pretrained models are pivotal in addressing the CD-FSL task owing to their exceptional representational and generalization prowess. To our knowledge, no existing research comprehensively investigates the utility of large-scale pretrained models in the CD-FSL context. Addressing this gap, our study presents an exhaustive empirical assessment of the Contrastive Language-Image Pre-Training model within the CD-FSL task. We undertake a comparison spanning six dimensions: base model, transfer module, classifier, loss, data augmentation, and training schedule. Furthermore, we establish a straightforward baseline model, E-base, based on our empirical analysis, underscoring the importance of our investigation. Experimental results substantiate the efficacy of our model, yielding a mean gain of 1.2% in 5-way 5-shot evaluations on the BSCD dataset. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Keyword ：
Adversarial machine learning Adversarial machine learning Contrastive Learning Contrastive Learning Zero-shot learning Zero-shot learning
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| GB/T 7714 | Zhuo, Linhai , Fu, Yuqian , Chen, Jingjing et al. Unified View Empirical Study for Large Pretrained Model on Cross-Domain Few-Shot Learning [J]. | ACM Transactions on Multimedia Computing, Communications and Applications , 2024 , 20 (9) . |
| MLA | Zhuo, Linhai et al. "Unified View Empirical Study for Large Pretrained Model on Cross-Domain Few-Shot Learning" . | ACM Transactions on Multimedia Computing, Communications and Applications 20 . 9 (2024) . |
| APA | Zhuo, Linhai , Fu, Yuqian , Chen, Jingjing , Cao, Yixin , Jiang, Yu-Gang . Unified View Empirical Study for Large Pretrained Model on Cross-Domain Few-Shot Learning . | ACM Transactions on Multimedia Computing, Communications and Applications , 2024 , 20 (9) . |
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