AbstractThe goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by few labeled samples. Most recent approaches adopt a single similarity measure, that is, global or local measure alone. However, for images with high intra-class variance and low inter-class variance, exploring invariant features discriminative details quite essential. In this paper...
