Transformer-based Clipped Contrastive Quantization Learning for
  Unsupervised Image Retrieval
Ayush Dubey Shiv Ram Dubey Satish Kumar Singh Wei-Ta Chu
Unsupervised image retrieval aims to learn the important visual
characteristics without any given level to retrieve the similar images for a
given query image. The Convolutional Neural Network (CNN)-based approaches have
been extensively exploited with self-supervised contrastive learning for image
hashing. However, the existing approaches suffer due to lack of effective
utilization of global features by CNNs and biased-ness created by false
negative pairs in the contrastive learning. In this paper, we propose a
TransClippedCLR model by encoding the global context of an image using
Transformer having local context through patch based processing, by generating
the hash codes through product quantization and by avoiding the potential false
negative pairs through clipped contrastive learning. The proposed model is
tested with superior performance for unsupervised image retrieval on benchmark
datasets, including CIFAR10, NUS-Wide and Flickr25K, as compared to the recent
state-of-the-art deep models. The results using the proposed clipped
contrastive learning are greatly improved on all datasets as compared to same
backbone network with vanilla contrastive learning.