October 2026

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

Cross-domain few-shot learning enables the transfer of knowledge from the source domain (SD) to the target domain (TD) with a limited number of annotated samples, proving highly effective in hyperspec

Deep Unfolding-based Networks (DUNs) have drawn substantial attention in Compressive Imaging (CI) tasks for their high performance and partial interpretability. Yet, existing DUNs typically cascade id

Parameter-Efficient Fine-Tuning (PEFT) has become a widely used paradigm for adapting large vision models to downstream tasks. However, many existing PEFT methods rely on low-rank or linear transforma

3D semantic occupancy prediction (OCC) is a crucial task in vision-centric autonomous driving, focusing on reconstructing fine-grained 3D geometry and semantics of the scene surrounding the ego vehicl

In recent years, multi-view multi-label learning (MVML) has attracted extensive attention due to its close alignment with real-world scenarios. Information-theoretic methods have gained prominence for