  What makes two images similar? We propose new approaches to generate
model-agnostic explanations for image similarity, search, and retrieval. In
particular, we extend Class Activation Maps (CAMs), Additive Shapley
Explanations (SHAP), and Locally Interpretable Model-Agnostic Explanations
(LIME) to the domain of image retrieval and search. These approaches enable
black and grey-box model introspection and can help diagnose errors and
understand the rationale behind a model's similarity judgments. Furthermore, we
extend these approaches to extract a full pairwise correspondence between the
query and retrieved image pixels, an approach we call "joint interpretations".
Formally, we show joint search interpretations arise from projecting Harsanyi
dividends, and that this approach generalizes Shapley Values and The
Shapley-Taylor indices. We introduce a fast kernel-based method for estimating
Shapley-Taylor indices and empirically show that these game-theoretic measures
yield more consistent explanations for image similarity architectures.
