Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly
focused on single-media retrieval. However, the requirements of users are highly flexible …

In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of
multimodal data. It takes one type of data as the query to retrieve relevant data of another …

Many articles have expounded on and defended the potential advantages of co-robotics
(cobots), robotics, AI, and quantum computers in the domains of research and development …

This paper attacks the challenging problem of video retrieval by text. In such a retrieval
paradigm, an end user searches for unlabeled videos by ad-hoc queries described …

Cross-modal retrieval aims to enable flexible retrieval across different modalities. The core
of cross-modal retrieval is how to measure the content similarity between different types of …

Cross-modal retrieval aims to enable flexible retrieval experience across different modalities
(eg, texts vs. images). The core of cross-modal retrieval research is to learn a common …

In this work, we investigate the problem of learning knowledge from the massive community-
contributed images with rich weakly-supervised context information, which can benefit …

Cross-modal hashing encodes the multimedia data into a common binary hash space in
which the correlations among the samples from different modalities can be effectively …

Recently, multi-view representation learning has become a rapidly growing direction in
machine learning and data mining areas. This paper introduces two categories for multi …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …