Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

Image colorization, as an essential problem in computer vision (CV), has attracted an
increasing amount of researchers attention in recent years, especially deep learning-based …

In recent years, there has been a proliferation of multimedia applications that leverage
machine learning (ML) for interactive experiences. Prototyping ML-based applications is …

Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results;
nonetheless, even thorough recordings yield imperfections in reconstructions, for instance …

Automatic image colorization is a challenging task that attracts a lot of research interest.
Previous methods employing deep neural networks have produced impressive results …

The goal of image colorization is to make the generated color images closely approximate
the color layout of the real color images. However, most of the existing methods do not …

Colorization is a process of converting grayscale images into visually acceptable color
images. The main goal is to convince the viewer of the authenticity of the result. Grayscale …

Invertible image decolorization is a useful color compression technique to reduce the cost in
multimedia systems. Invertible decolorization aims to synthesize faithful grayscales from …

Underwater images frequently suffer from color casts and poor contrast, due to the
absorption and scattering of light in water medium. To address these two degradation …

Most humans use visual imagination to understand and reason about language, but models
such as BERT reason about language using knowledge acquired during text-only …