Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval
This work improves unsupervised image retrieval for applications needing efficient similarity search, though it is incremental as it builds on existing contrastive learning and quantization techniques.
The paper tackles unsupervised image retrieval by addressing limitations in CNN-based methods and false negatives in contrastive learning, proposing a Transformer-based model with clipped contrastive learning that achieves superior performance on benchmark datasets like CIFAR10, NUS-Wide, and Flickr25K.
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.