October 2026

Recent advances in unconditional diffusion models have shown remarkable performance in conditional generation tasks. Typically, diffusion models serve as priors, incorporating differentiable loss func

In medical image segmentation, accurate recognition of regions hindered by blurry boundaries, low contrast, and complex backgrounds is crucial. These issues lead to significant differences in segmenta

One fundamental problem of inverse lithography is to evolve the optimized mask function which represents the mask using continuous tones to assume allowed mask values. To this end, a solution-dependen

With the rapid advancement of generative AI, large-scale text-to-image models are widely used in content creation. Creative demands are shifting from single images to serialized expressions, leading t

Hyperspectral object tracking leverages its rich spectral material identification capabilities to address challenges that RGB video alone struggles to overcome, such as camouflaged targets and clutter

Portrait cast shadow removal is a critical yet challenging task in computer vision and portrait editing. Current methods mainly rely on planar features, such as facial landmarks, to assist in cast sha

In recent years, the rapid development of generative artificial intelligence technology has significantly lowered the barrier to creating high-quality fake images, posing a serious challenge to inform

Geometric estimation in computer vision often struggles with balancing speed and accuracy, especially when confronted with high outlier ratios or challenging viewpoint changes inherent in real-world d

Semi-supervised medical image segmentation aims to reduce the burden of manual annotation by leveraging a limited set of labeled data along with a large collection of unlabeled images, while maintaini

Multi-label remote sensing (RS) scene classification aims to describe RS images according to the contained land covers. Existing methods typically focus on two aspects: visual learning to extract clas