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 151160 of 195 papers

TitleStatusHype
Texture Retrieval via the Scattering Transform0
Medical Image Super-Resolution Using a Generative Adversarial Network0
The Shortlist Method for Fast Computation of the Earth Mover's Distance and Finding Optimal Solutions to Transportation Problems0
Towards Practical Visual Search Engine within Elasticsearch0
Triagem virtual de imagens de imuno-histoquímica usando redes neurais artificiais e espectro de padrões0
Unsupervised Multi-modal Hashing for Cross-modal retrieval0
Unsupervised Content based Image Retrieval at Different Precision Level by Combining Multiple Features0
VISIR: Visual and Semantic Image Label Refinement0
Visual descriptors for content-based retrieval of remote sensing images0
Visual Relationship Detection with Language Priors0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94Unverified