Match Them Up: Visually Explainable Few-shot Image Classification

Understand

Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories.

• However, there is no guarantee, especially for the latter part.
• This issue leads to the unknown nature of the inference process in most FSL methods, which hampers its application in some risk-sensitive areas.
• In this paper, we reveal a new way to perform FSL for image classification, using visual representations from the backbone model and weights generated by a newly-emerged explainable classifier.