  It has been observed that visual classification models often rely mostly on
the image background, neglecting the foreground, which hurts their robustness
to distribution changes. To alleviate this shortcoming, we propose to monitor
the model's relevancy signal and manipulate it such that the model is focused
on the foreground object. This is done as a finetuning step, involving
relatively few samples consisting of pairs of images and their associated
foreground masks. Specifically, we encourage the model's relevancy map (i) to
assign lower relevance to background regions, (ii) to consider as much
information as possible from the foreground, and (iii) we encourage the
decisions to have high confidence. When applied to Vision Transformer (ViT)
models, a marked improvement in robustness to domain shifts is observed.
Moreover, the foreground masks can be obtained automatically, from a
self-supervised variant of the ViT model itself; therefore no additional
supervision is required.
