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 41–50 of 195 papers

TitleStatusHype
BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval—0
Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans—0
NORPPA: NOvel Ringed seal re-identification by Pelage Pattern AggregationCode0
Constrained Mass Optimal Transport—0
Deep Features for CBIR with Scarce Data using Hebbian Learning—0
Scaling Cross-Domain Content-Based Image Retrieval for E-commerce Snap and Search Application—0
Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection—0
A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images—0
A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval—0
Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image RepresentationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified