  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
experiments carried on inductive FSL tasks demonstrate the effectiveness of our
approaches.
