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
AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image MatchingCode1
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
GPR1200: A Benchmark for General-Purpose Content-Based Image RetrievalCode1
Contextual Similarity Aggregation with Self-attention for Visual Re-rankingCode1
Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified