While transformers have indeed improved image retrieval accuracy in computer vision,
challenges persist, including insufficient and imbalanced feature extraction and the inability …

When performing hashing-based image retrieval, it is difficult to learn discriminative hash
codes especially for the multi-label, zero-shot and fine-grained settings. This is due to the …

The current trend in hashing methods entails utilizing Convolutional Neural Networks
(CNNs) to extract high-dimensional semantic information and transform it into binary hash …

Retrieving similar images is becoming an urgent need for us with the continuous growth of
large-scale data. However, whether the dominant image retrieval methods are …

In view of the problems of insufficient feature extraction and ineffective capture of correlation
between deep features in existing image retrieval methods, a modified dual attention triplet …

Deep hashing is an effective method for content-based image retrieval due to its low storage
requirements and fast retrieval speed. However, deep hashing is known to suffer from …

Deep Hashing is a technique used for retrieving images on a large-scale, encoding the
latent code of images into binary codes, which significantly reduces computational and …