Large-scale vision-language models (VLMs) like CLIP successfully find correspondences
between images and text. Through the standard deterministic mapping process, an image or …

High-quality calibrated uncertainty estimates are crucial for numerous real-world
applications, especially for deep learning-based deployed ML systems. While Bayesian …

Multimodal machine learning has achieved remarkable progress in a wide range of
scenarios. However, the reliability of multimodal learning remains largely unexplored. In this …

Existing multi-modal image fusion methods utilize multi-modal images as input that require
multiple imaging of patients causing harm to patients' bodies and large costs, moreover …

A considerable amount of mobility data has been accumulated due to the proliferation of
location-based services. Nevertheless, compared with mobility data from transportation …

Image-to-image translation plays a vital role in tackling various medical imaging tasks such
as attenuation correction, motion correction, undersampled reconstruction, and denoising …

Recent uncertainty quantification approaches lack transparency. Algorithms often perform
poorly in an input domain and the reason for poor performance remains unknown …

Unsupervised image transfer enables intra-and inter-modality image translation in
applications where a large amount of paired training data is not abundant. To ensure a …

Video frame interpolation can flexibly increase the temporal resolution of low frame-rate
videos by generating the missing intermediate frames at any time. Existing methods …

Dense regression is a widely used approach in computer vision for tasks such as image
super-resolution, enhancement, depth estimation, etc. However, the high cost of annotation …