As a common image editing operation, image composition aims to combine the foreground
from one image and another background image, resulting in a composite image. However …

Training deep learning-based change detection (CD) models heavily relies on large labeled
data sets. However, it is time-consuming and labor-intensive to collect large-scale …

The field of smooth vector graphics explores the representation, creation, rasterization, and
automatic generation of light-weight image representations, frequently used for scalable …

Image composition is an important operation in image processing, but the inconsistency
between foreground and background significantly degrades the quality of composite image …

While generative adversarial networks (GANs) can successfully produce high-quality
images, they can be challenging to control. Simplifying GAN-based image generation is …

Image harmonization aims to improve the quality of image compositing by matching the"
appearance""(eg, color tone, brightness and contrast) between foreground and background …

Wasserstein distance plays increasingly important roles in machine learning, stochastic
programming and image processing. Major efforts have been under way to address its high …

This article introduces a generalization of the discrete optimal transport, with applications to
color image manipulations. This new formulation includes a relaxation of the mass …

Accurate building change detection is crucial for understanding urban development.
Although fully supervised deep learning-based methods for building change detection have …

Recent advances in 3D scene representation and novel view synthesis have witnessed the
rise of Neural Radiance Fields (NeRFs). Nevertheless, it is not trivial to exploit NeRF for the …