Supervised Image Retrieval
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Benchmarks
CIFAR-10
Most implemented
Unicom: Universal and Compact Representation Learning for Image Retrieval
Generalized Product Quantization Network for Semi-supervised Image Retrieval
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
Papers
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Image retrieval, i.e., finding desired images given a reference image, inherently encompasses rich, multi-faceted search intents that are difficult to capture solely using image-based measures. Recent works leverage text…
Image RetrievalImplicit RelationsRetrievalSupervised Image Retrieval+2Unicom: Universal and Compact Representation Learning for Image Retrieval
Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract image-level descriptors. However, the most widely used models are pre-trained on ImageNet-1K with limited classes. The pre-trai…
Image ClassificationImage RetrievalMetric LearningRepresentation Learning+3Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
Semantic hashing is an emerging technique for large-scale similarity search based on representing high-dimensional data using similarity-preserving binary codes used for efficient indexing and search. It has recently bee…
Supervised Image RetrievalSupervised Text RetrievalGeneralized Product Quantization Network for Semi-supervised Image Retrieval
Image retrieval methods that employ hashing or vector quantization have achieved great success by taking advantage of deep learning. However, these approaches do not meet expectations unless expensive label information i…
Image RetrievalMetric LearningQuantizationRetrieval+3