  We consider the problem of visually explaining similarity models, i.e.,
explaining why a model predicts two images to be similar in addition to
producing a scalar score. While much recent work in visual model
interpretability has focused on gradient-based attention, these methods rely on
a classification module to generate visual explanations. Consequently, they
cannot readily explain other kinds of models that do not use or need
classification-like loss functions (e.g., similarity models trained with a
metric learning loss). In this work, we bridge this crucial gap, presenting a
method to generate gradient-based visual attention for image similarity
predictors. By relying solely on the learned feature embedding, we show that
our approach can be applied to any kind of CNN-based similarity architecture,
an important step towards generic visual explainability. We show that our
resulting attention maps serve more than just interpretability; they can be
infused into the model learning process itself with new trainable constraints.
We show that the resulting similarity models perform, and can be visually
explained, better than the corresponding baseline models trained without these
constraints. We demonstrate our approach using extensive experiments on three
different kinds of tasks: generic image retrieval, person re-identification,
and low-shot semantic segmentation.
