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

Showing 110 of 195 papers

TitleStatusHype
PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural NetworksCode2
Leveraging Foundation Models for Content-Based Medical Image Retrieval in RadiologyCode1
Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote SensingCode1
A clinically motivated self-supervised approach for content-based image retrieval of CT liver imagesCode1
Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based FeaturesCode1
AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image MatchingCode1
GPR1200: A Benchmark for General-Purpose Content-Based Image RetrievalCode1
Contextual Similarity Aggregation with Self-attention for Visual Re-rankingCode1
Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold RankingCode1
Class Agnostic Instance-level Descriptor for Visual Instance Search0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94Unverified