September 2026

AI-generated image quality assessment (AIGIQA) requires jointly reasoning about perceptual fidelity and prompt alignment, two quality dimensions that are often treated as independent in existing AIGIQ

Few-shot defect segmentation (FSDS) has emerged as an effective approach to address the challenge of limited annotated samples in industrial quality inspection. However, existing methods often rely on

Accurately identifying priority attention regions in complex driving scenarios is critical yet challenging, due to the coupling of heterogeneous factors such as motion, structured semantics, and human

Multi-phase contrast-enhanced CT (CECT) is widely employed to capture the dynamic enhancement patterns and temporal evolution of organs and lesions. However, acquiring multiple phases increases radiat

Occluded by rain streaks and haze, rainy images suffer from information loss, which necessitates the precise recovery of missing content. However, visually appealing yet spurious image details generat

Hyperspectral image classification (HSIC) has witnessed remarkable progress with the rise of deep learning. Nevertheless, its real-world deployment remains substantially constrained by two fundamental

Organic light-emitting diode (OLED)-based augmented and virtual reality (AR/VR) technologies are becoming increasingly integral to everyday life. However, these devices often face significant challeng

Deep Neural Networks (DNNs) remain vulnerable to adversarial perturbations, raising significant concerns in image processing applications, particularly in high-stakes domains such as medical imaging a

Facial emotion recognition (FER) has traditionally focused on a limited set of basic expressions, often failing to capture the complexity, subtlety, and cultural variability of real-world human emotio

In semi-supervised object detection (SSOD), due to the limited availability of labeled data, the quality and quantity of pseudo labels generated from unlabeled images are crucial for model training. O