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Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval
[article]
2022
arXiv
pre-print
Unsupervised image retrieval aims to learn an efficient retrieval system without expensive data annotations, but most existing methods rely heavily on handcrafted feature descriptors or pre-trained feature extractors. To minimize human supervision, recent advance proposes deep fully unsupervised image retrieval aiming at training a deep model from scratch to jointly optimize visual features and quantization codes. However, existing approach mainly focuses on instance contrastive learning
arXiv:2206.09806v1
fatcat:ha4hj3oxcvfvrbbhbdsbawjhhu