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 141–150 of 195 papers

TitleStatusHype
Knowledge Aware Semantic Concept Expansion for Image-Text Matching—0
Large-margin Learning of Compact Binary Image Encodings—0
Large Scale Deep Convolutional Neural Network Features Search with Lucene—0
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval—0
Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling—0
Learning Hash Function through Codewords—0
Learning Non-Metric Visual Similarity for Image Retrieval—0
Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval—0
Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans—0
Lesion Search with Self-supervised Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified