Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …

Most modern approaches in temporal action localization divide this problem into two parts:(i)
short-term feature extraction and (ii) long-range temporal boundary localization. Due to the …

Abstract The" Patient Instruction"(PI), which contains critical instructional information
provided both to carers and to the patient at the time of discharge, is essential for the patient …

Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality
videos with higher resolution and higher frame rates. Existing advanced methods …

Remote sensing image dehazing (RSID) is an extremely challenging problem due to the
irregular and nonuniform distribution of haze. The existing RSID methods achieve excellent …

Although recent video super-resolution (VSR) works show remarkable restoration
performance for low-resolution (LR) video downscaled by a fixed known blur kernel, blind …

Light field (LF) cameras suffer from a fundamental trade-off between spatial and angular
resolutions. Additionally, due to the significant amount of data that needs to be recorded, the …

The target of space–time video super-resolution (STVSR) is to increase the spatial–temporal
resolution of low-resolution (LR) and low-frame-rate (LFR) videos. Recent approaches …

Given multiple videos of the same task, procedure learning addresses identifying the key-
steps and determining their order to perform the task. For this purpose, existing approaches …

Objective. Metal artifact reduction (MAR) has been a key issue in CT imaging. Recently,
MAR methods based on deep learning have achieved promising results. However, when …