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 …

With an exponential increase in the provisioning of multimedia devices over the Internet of
Things (IoT), a significant amount of multimedia data (also referred to as multimedia big data …

It is known that the inconsistent distributions and representations of different modalities, such
as image and text, cause the heterogeneity gap, which makes it very challenging to correlate …

In this paper, we present a new multimedia retrieval paradigm to innovate large-scale
search of heterogenous multimedia data. It is able to return results of different media types …

Cross-modal retrieval has recently drawn much attention due to the widespread existence of
multimodal data. It takes one type of data as the query to retrieve relevant data objects of …

The problem of cross-modal retrieval from multimedia repositories is considered. This
problem addresses the design of retrieval systems that support queries across content …

Cross-modal retrieval has become a highlighted research topic for retrieval across
multimedia data such as image and text. A two-stage learning framework is widely adopted …

Cross-media retrieval has become a key problem in both research and application, in which
users can search results across all of the media types (text, image, audio, video, and 3-D) by …

We present a new framework for multimedia content analysis and retrieval which consists of
two independent algorithms. First, we propose a new semi-supervised algorithm called …

Cross-modal hashing has been widely used in multimedia retrieval tasks due to its fast
retrieval speed and low storage cost. Recently, many deep unsupervised cross-modal …