SOTAVerified

Content-Based Image Retrieval

Content-Based Image Retrieval is a well studied problem in computer vision, with retrieval problems generally divided into two groups: category-level retrieval and instance-level retrieval. Given a query image of the Sydney Harbour bridge, for instance, category-level retrieval aims to find any bridge in a given dataset of images, whilst instance-level retrieval must find the Sydney Harbour bridge to be considered a match.

Source: Camera Obscurer: Generative Art for Design Inspiration

Papers

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Showing 21–30 of 195 papers

TitleStatusHype
Lesion Search with Self-supervised Learning—0
Integrating Visual and Semantic Similarity Using Hierarchies for Image RetrievalCode0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotations—0
Class Anchor Margin Loss for Content-Based Image Retrieval—0
Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval—0
A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval—0
iQPP: A Benchmark for Image Query Performance PredictionCode0
Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss—0
Multimorbidity Content-Based Medical Image Retrieval Using Proxies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified