Matching images and sentences demands a fine understanding of both modalities. In this
article, we propose a new system to discriminatively embed the image and text to a shared …

Hashing methods have been extensively applied to efficient multimedia data indexing and
retrieval on account of the explosion of multimedia data. Cross-modal hashing usually …

Multi-scale feature extraction is crucial for many computer vision tasks, but it is rarely
explored in Transformer-based image super-resolution (SR) methods. In this paper, we …

Binary code learning has been emerging topic in large-scale cross-modality retrieval
recently. It aims to map features from multiple modalities into a common Hamming space …

Multi-modal clustering represents a formidable challenge in the domain of unsupervised
learning. The objective of multi-modal clustering is to categorize data collected from diverse …

In this paper, we study the problem of cross-modal retrieval by hashing-based approximate
nearest neighbor search techniques. Most existing cross-modal hashing works mainly …

The amount of multi-modal data available on the Internet is enormous. Cross-modal hash
retrieval maps heterogeneous cross-modal data into a single Hamming space to offer fast …

Nowadays, cross-modal retrieval plays an important role to flexibly find useful information
across different modalities of data. Effectively measuring the similarity between different …

After the COVID-19 pandemic, no one refutes the importance of smart online learning
systems in the educational process. Measuring student engagement is a crucial step …

Recently, an increasingly pervasive trend in real-word applications is that the data are
collected from multiple sources or represented by multiple views. Owing to the powerful …