Supervised deep learning-based hash and vector quantization are enabling fast and large-
scale image retrieval systems. By fully exploiting label annotations, they are achieving …

In the field of computer vision, fine-grained image retrieval is an extremely challenging task
due to the inherently subtle intra-class object variations. In addition, the high-dimensional …

Given a text/image query, image-text retrieval aims to find the relevant items in the database.
Recently, visual-linguistic pre-training (VLP) methods have demonstrated promising …

The focus of this study is on Unsupervised Continual Learning (UCL), as it presents an
alternative to Supervised Continual Learning which needs high-quality manual labeled data …

Deep hashing approaches, including deep quantization and deep binary hashing, have
become a common solution to large-scale image retrieval due to their high computation and …

With the recent boom of video-based social platforms (eg, YouTube and TikTok), video
retrieval using sentence queries has become an important demand and attracts increasing …

Unsupervised video hashing aims to learn a nonlinear hashing function to map videos into a
similarity-preserving hamming space without label supervision. Different from static images …

Unsupervised cross-modal hashing (UCMH) has been commonly explored to support large-
scale cross-modal retrieval of unlabeled data. Despite promising progress, most existing …

Existing deep quantization methods provided an efficient solution for large-scale image
retrieval. However, the significant intra-class variations, like pose, illumination, and …

Hashing and quantization have greatly succeeded by benefiting from deep learning for large-
scale image retrieval. Recently, deep product quantization methods have attracted wide …