  Two primary input modalities prevail in image retrieval: sketch and text.
While text is widely used for inter-category retrieval tasks, sketches have
been established as the sole preferred modality for fine-grained image
retrieval due to their ability to capture intricate visual details. In this
paper, we question the reliance on sketches alone for fine-grained image
retrieval by simultaneously exploring the fine-grained representation
capabilities of both sketch and text, orchestrating a duet between the two. The
end result enables precise retrievals previously unattainable, allowing users
to pose ever-finer queries and incorporate attributes like colour and
contextual cues from text. For this purpose, we introduce a novel
compositionality framework, effectively combining sketches and text using
pre-trained CLIP models, while eliminating the need for extensive fine-grained
textual descriptions. Last but not least, our system extends to novel
applications in composed image retrieval, domain attribute transfer, and
fine-grained generation, providing solutions for various real-world scenarios.
