SOTAVerified

Composed Image Retrieval (CoIR)

Composed Image Retrieval (CoIR) is the task involves retrieving images from a large database based on a query composed of multiple elements, such as text, images, and sketches. The goal is to develop algorithms that can understand and combine multiple sources of information to accurately retrieve images that match the query, extending the user’s expression ability.

Papers

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Showing 1–14 of 14 papers

TitleStatusHype
Composed Image Retrieval for Remote SensingCode2
iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image RetrievalCode2
Sentence-level Prompts Benefit Composed Image RetrievalCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal EncoderCode1
Bi-directional Training for Composed Image Retrieval via Text Prompt LearningCode1
Zero-Shot Composed Image Retrieval with Textual InversionCode1
CompoDiff: Versatile Composed Image Retrieval With Latent DiffusionCode1
Data Roaming and Quality Assessment for Composed Image RetrievalCode1
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image RetrievalCode1
Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty RegularizationCode1
Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based FeaturesCode1
Effective Conditioned and Composed Image Retrieval Combining CLIP-Based FeaturesCode1
Image Retrieval on Real-life Images with Pre-trained Vision-and-Language ModelsCode1
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All datasetsCIRRFashion IQ

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoVR-BLIP-2R@150.43—Unverified
#ModelMetricClaimedVerifiedStatus
1CoVR-BLIP-2(Recall@10+Recall@50)/260.57—Unverified