In this paper, we study the problem of temporal video grounding (TVG), which aims to predict
the starting/ending time points of moments described by a text sentence within a long …

Deep learning (DL) methods have been extensively employed in magnetic resonance
imaging (MRI) reconstruction, demonstrating remarkable performance improvements …

As machine learning tasks continue to evolve, the trend has been to gather larger datasets
and train increasingly larger models. While this has led to advancements in accuracy, it has …

The recovery of magnetic resonance (MR) images from undersampled measurements is a
key problem that has been the subject of extensive research in recent years. Unrolled …

Deep learning (DL) techniques have been extensively employed in magnetic resonance
imaging (MRI) reconstruction, delivering notable performance enhancements over …

As the popularity of deep learning (DL) in the field of magnetic resonance imaging (MRI)
continues to rise, recent research has indicated that DL-based MRI reconstruction models …

Radio telescopes produce visibility data about celestial objects, but these data are sparse
and noisy. As a result, images created on raw visibility data are of low quality. Recent …

Deep learning (DL) methods, especially those based on physics-driven DL, have become
the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data …

Magnetic resonance imaging (MRI) is an imaging modality widely used in clinical practice.
MRI images are reconstructed from spatial frequency measurements that are acquired …