Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is
advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) …

In the realm of video object tracking auxiliary modalities such as depth thermal or event data
have emerged as valuable assets to complement the RGB trackers. In practice most existing …

In remote sensing (RS), multiple modalities of data are usually available, eg, RGB,
multispectral, hyperspectral, light detection and ranging (LiDAR), and synthetic aperture …

Abstract In Re-identification (ReID) recent advancements yield noteworthy progress in both
unimodal and cross-modal retrieval tasks. However the challenge persists in develo** a …

The combination of electronic health records (EHR) and medical images is crucial for
clinicians in making diagnoses and forecasting prognoses. Strategically fusing these two …

Multi-modal signals have become essential data for emotion recognition since they can
represent emotions more comprehensively. However, in real-world environments, it is often …

Multimodal Sentiment Analysis (MSA) has attracted widespread research attention recently.
Most MSA studies are based on the assumption of signal completeness. However, many …

Human state recognition is a critical topic with pervasive and important applications in
human–machine systems. Multimodal fusion, which entails integrating metrics from various …

Multimodal learning typically relies on the assumption that all modalities are fully available
during both the training and inference phases. However, in real-world scenarios …

Object detection in visible (RGB) and infrared (IR) images has been widely applied in recent
years. Leveraging the complementary characteristics of RGB and IR images, the object …