Rapid advances in artificial intelligence (AI) in the last decade have been largely built upon
the wide applications of deep learning (DL). However, the high carbon footprint yielded by …

Artificial intelligence (AI) applications in medicine and healthcare have been growing rapidly
in recent years. More clinician-scientists have been interested in engaging in the medical AI …

No-Reference Image Quality Assessment aims to evaluate the perceptual quality of an
image, according to human perception. Many recent studies use Transformers to assign …

While extensive research has been conducted on evaluating generative models, little
research has been conducted on the quality assessment and enhancement of individual …

Although there are metrics to evaluate the performance of generative models, little research
is conducted on the quality evaluation of individual generated samples. A lightweight …

Blind image quality assessment (BIQA) is a task that predicts the perceptual quality of an
image without its reference. Research on BIQA attracts growing attention due to the …

Image saliency detection is crucial in understanding human gaze patterns from visual
stimuli. The escalating demand for research in image saliency detection is driven by the …

Image quality assessment is a vital computer vision task for image validation and visual
experience development. Lately, most research in this field has focused on enhancing …

AI algorithms at the edge demand smaller model sizes and lower computational complexity.
To achieve these objectives, we adopt a green learning (GL) paradigm rather than the deep …

As a new paradigm of machine learning, green learning has achieved performance
comparable to deep learning in vision tasks, knowledge graph learning, and modeling …