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 161170 of 195 papers

TitleStatusHype
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation0
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function0
Pareto-depth for Multiple-query Image Retrieval0
PinView: Implicit Feedback in Content-Based Image Retrieval0
Radiological images and machine learning: trends, perspectives, and prospects0
Radon Features and Barcodes for Medical Image Retrieval via SVM0
RBIR using Interest Regions and Binary Signatures0
Recent Advance in Content-based Image Retrieval: A Literature Survey0
REJEPA: A Novel Joint-Embedding Predictive Architecture for Efficient Remote Sensing Image Retrieval0
Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotations0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94Unverified