September 2026

In frame interpolation tasks, motion ambiguity in the training set causes models to generate blurry intermediate frames. Moreover, the assumption of uniform motion between frames during inference furt

The moiré effect, a common artifact when photographing digital screens, degrades images with a complex mixture of geometric aliasing patterns and severe photometric distortions. These degradations col

Multimodal Large Language Models (MLLMs), such as GPT4o, have demonstrated strong capabilities in visual reasoning and explanation generation. However, despite these advantages, they face notable chal

Standard CMOS imagers provide reliable real-time imaging only above 1 lx, typical of daylight. Electron Bombarded Active Pixel Sensor (EBAPS), employing photoelectron bombardment gain, delivers clear

Division-of-focal-plane polarimeter (DoFP) cameras enable real-time polarization imaging by simultaneously capturing multiple polarization orientations at the cost of spatial resolution. Existing conv

Hyperspectral image (HSI) classification often suffers from the scarcity of labeled samples. Semi-supervised learning (SSL) alleviates this issue by jointly exploiting limited labeled samples and abun

Few-shot learning (FSL) provides a promising solution for reducing annotation cost in hyperspectral image change detection (HSI-CD). However, most existing FSL approaches focus on single-modality or h

Pseudo-label self-training is a cornerstone of Source-Free Domain Adaptation (SFDA) in medical imaging, valued for improving performance while protecting data privacy. However, its effectiveness is of

Skeleton-based action recognition has gained increasing attention due to its robustness to viewpoint variations and background clutter. However, existing approaches still face challenges in feature mo

Adversarial training, one of the most effective methods for enhancing neural network robustness, is typically formulated as a min-max game between an attacker and a defender. Despite its success, most