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Zero-Shot Composed Image Retrieval (ZS-CIR)

Given a query composed of a reference image and a relative caption, Composed Image Retrieval (CIR) aims to retrieve target images that are visually similar to the reference one but incorporate the changes specified in the relative caption. The bi-modality of the query provides users with more precise control over the characteristics of the desired image, as some features are more easily described with language, while others can be better expressed visually.

Zero-Shot Composed Image Retrieval (ZS-CIR) is a subtask of CIR that aims to design an approach that manages to combine the reference image and the relative caption without the need for supervised learning.

Papers

Showing 1120 of 36 papers

TitleStatusHype
Zero-shot Composed Text-Image RetrievalCode1
CoLLM: A Large Language Model for Composed Image RetrievalCode1
CompoDiff: Versatile Composed Image Retrieval With Latent DiffusionCode1
Composed Image Retrieval for Training-Free Domain ConversionCode1
Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image RetrievalCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective ReasoningCode1
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and NegativesCode1
Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image RetrievalCode1
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image RetrievalCode1
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