In recent years, information available from multiple data modalities has become increasingly
common for industrial engineering and operations research applications. There have been a …

With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval
methods struggle to meet the needs of users seeking access to data across various …

The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …

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 …

Infrared and visible image fusion aims to utilize the complementary information between the
two modalities to synthesize a new image containing richer information. Most existing works …

The task of image-text matching refers to measuring the visual-semantic similarity between
an image and a sentence. Recently, the fine-grained matching methods that explore the …

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 …

Given a query instance from one modality (eg, image), cross-modal retrieval aims to find
semantically similar instances from another modality (eg, text). To perform cross-modal …

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 …