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[bibtex]@InProceedings{Wang_2021_CVPR, author = {Wang, Bowen and Li, Liangzhi and Verma, Manisha and Nakashima, Yuta and Kawasaki, Ryo and Nagahara, Hajime}, title = {MTUNet: Few-Shot Image Classification With Visual Explanations}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {2294-2298} }
MTUNet: Few-Shot Image Classification With Visual Explanations
Abstract
Few-shot learning (FSL) approaches, mostly neural network-based, are assuming that the pre-trained knowledge can be obtained from base (seen) categories and transferred to novel (unseen) categories. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper its application in some risk-sensitive areas. In this paper, we reveal a new way to perform explainable FSL for image classification, using discriminative patterns and pairwise matching. Experimental results prove that the proposed method can achieve satisfactory explainability on two mainstream datasets. Code is available at https://github.com/wbw520/MTUNet.
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