This paper develops a unified framework for image-to-image translation based on
conditional diffusion models and evaluates this framework on four challenging image-to …

We present the Colorization Transformer, a novel approach for diverse high fidelity image
colorization based on self-attention. Given a grayscale image, the colorization proceeds in …

In this work, we address the task of natural image generation guided by a conditioning input.
We introduce a new architecture called conditional invertible neural network (cINN). The …

Colorization has attracted increasing interest in recent years. Classic reference-based
methods usually rely on external color images for plausible results. A large image database …

We introduce UViM, a unified approach capable of modeling a wide range of computer
vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; …

This paper presents the first end-to-end network for exemplar-based video colorization. The
main challenge is to achieve temporal consistency while remaining faithful to the reference …

Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous
methods leverage the deep neural network to map input grayscale images to plausible color …

Given a grayscale photograph, the colorization system estimates a visually plausible colorful
image. Conventional methods often use semantics to colorize grayscale images. However …

While many image colorization algorithms have recently shown the capability of producing
plausible color versions from gray-scale photographs, they still suffer from limited semantic …

Because thermal infrared (TIR) images are not affected by light and foggy environments,
which are widely used in various night traffic scenarios. Especially, thermal infrared images …