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 181190 of 195 papers

TitleStatusHype
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and RetrievalCode0
NORPPA: NOvel Ringed seal re-identification by Pelage Pattern AggregationCode0
OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates DiscoveryCode0
City-Scale Visual Place Recognition with Deep Local Features Based on Multi-Scale Ordered VLAD PoolingCode0
Gray Level Co-Occurrence Matrices: Generalisation and Some New FeaturesCode0
Hash Function Learning via CodewordsCode0
SIFT Meets CNN: A Decade Survey of Instance RetrievalCode0
iART: A Search Engine for Art-Historical Images to Support Research in the HumanitiesCode0
Classification is a Strong Baseline for Deep Metric LearningCode0
Dual-Path Convolutional Image-Text Embeddings with Instance LossCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94Unverified