Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

Information fusion is a key step in multimodal biometric systems. The fusion of information
can occur at different levels of a recognition system, ie, at the feature level, matching-score …

Conventional visual perception technology is subject to many restrictions, such as
illumination, background clutter, and occlusion. Many intrinsic properties of objects, like …

Medical image fusion is important in image-guided medical diagnostics, treatment, and other
computer vision tasks. However, most current approaches assume that the source images …

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by
enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of …

Contrastive learning-based vision-language pre-training approaches, such as CLIP, have
demonstrated great success in many vision-language tasks. These methods achieve cross …

In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face,
iris, and fingerprint) for person identification. The proposed deep multimodal fusion …

Micro-videos have rapidly become one of the most dominant trends in the era of social
media. Accordingly, how to organize them draws our attention. Distinct from the traditional …

Different from traditional long videos, micro-videos are much shorter and usually recorded at
a specific place with mobile devices. To better understand the semantics of a micro-video …

With the advent of multi-view data, multi-view learning has become an important research
direction in machine learning and image processing. Considering the difficulty of obtaining …