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• W4317495847 abstract "Interactive image retrieval is such task setting where a multi-modal query (reference image, feedback text) is provided, and the goal is to retrieve a target image which satisfies the changes described in feedback text based on the reference image. It offers a great promise for better user experience in a variety of fields such as e-commerce where the user can address their need with natural language and find the desired item iteratively. With the rising of Vision-Language Pre-trained(VLP) models, it has become a de facto to transfer rich knowledge learned from large-scale real-world data to downstream tasks. In this work, we propose a novel method called InterCLIP, which adapt the matching oriented VLP model CLIP, to the task. To further harness the power of CLIP, we propose to view the task as a combination of text-image retrieval and standard image search. Specifically we calculate candidate images’ similarity score with similarity within the triplet. This method allows fine-grained modelling which takes account of the relevance between three pairs within the triplet, and extensive experiments show our method achieves state-of-the-art results on the FashionIQ dataset." @default.
• W4317495847 created "2023-01-20" @default.
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• W4317495847 date "2022-11-26" @default.
• W4317495847 modified "2023-09-27" @default.
• W4317495847 title "InterCLIP: Adapting CLIP To Interactive Image Retrieval with Triplet Similarity" @default.
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• W4317495847 doi "https://doi.org/10.1109/ccis57298.2022.10016349" @default.
• W4317495847 hasPublicationYear "2022" @default.
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