Rectifying the Shortcut Learning of Background for Few-Shot Learning. (arXiv:2107.07746v3 [cs.CV] UPDATED)

The category gap between training and evaluation has been characterised as
one of the main obstacles to the success of Few-Shot Learning (FSL). In this
paper, we for the first time empirically identify image background, common in
realistic images, as a shortcut knowledge helpful for in-class classification
but ungeneralizable beyond training categories in FSL. A novel framework,
COSOC, is designed to tackle this problem by extracting foreground objects in
images at both training and evaluation without any extra supervision. Extensive …

arxiv cv few-shot learning learning