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An intuitive way to search for images is to use queries composed of anexample image and a complementary text. While the first provides rich andimplicit context for the search, the latter explicitly calls for new traits, orspecifies how some elements of the example image should be changed to retrievethe desired target image. Current approaches typically combine the features ofeach of the two elements of the query into a single representation, which canthen be compared to the ones of the potential target images. Our work aims atshedding new light on the task by looking at it through the prism of twofamiliar and related frameworks: text-to-image and image-to-image retrieval.Taking inspiration from them, we exploit the specific relation of each queryelement with the targeted image and derive light-weight attention mechanismswhich enable to mediate between the two complementary modalities. We validateour approach on several retrieval benchmarks, querying with images and theirassociated free-form text modifiers. Our method obtains state-of-the-artresults without resorting to side information, multi-level features, heavypre-training nor large architectures as in previous works.

ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity