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You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval

Subhadeep Koley · Ayan Kumar Bhunia · Aneeshan Sain · Pinaki Nath Chowdhury · Tao Xiang · Yi-Zhe Song

Arch 4A-E Poster #186
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Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT


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.

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