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We address the problem of retrieving images with both a sketch and a textquery. We present TASK-former (Text And SKetch transformer), an end-to-endtrainable model for image retrieval using a text description and a sketch asinput. We argue that both input modalities complement each other in a mannerthat cannot be achieved easily by either one alone. TASK-former follows thelate-fusion dual-encoder approach, similar to CLIP, which allows efficient andscalable retrieval since the retrieval set can be indexed independently of thequeries. We empirically demonstrate that using an input sketch (even a poorlydrawn one) in addition to text considerably increases retrieval recall comparedto traditional text-based image retrieval. To evaluate our approach, we collect5,000 hand-drawn sketches for images in the test set of the COCO dataset. Thecollected sketches are available a https://janesjanes.github.io/tsbir/.

A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch