October 2026

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

Most existing GAN inversion methods aim to strike a good trade-off between fidelity and editability. However, a pre-trained GAN latent space only encodes information from in-domain regions while the i

The pursuit of large receptive fields has shaped the evolution of computer vision frameworks, spanning from convolutional neural networks (CNNs) to Transformers and Mamba. Recently, large-kernel opera

As a fundamental image processing task, object Re-Identification (ReID) aims to retrieve objects across non-overlapping cameras. Recently, with the development of deep learning, significant advancemen

Accurately assessing the perceptual quality of ultra-high-definition (UHD) images degraded by real-world noise remains a formidable challenge in computer vision. Existing Blind Image Quality Assessmen

Recent deep models for image shadow removal often rely on attention-based architectures to capture long-range dependencies. However, their fixed attention patterns tend to mix illumination cues from i

RGB-Thermal semantic segmentation is essential for robust visual understanding in autonomous driving, surveillance, and search-and-rescue applications. While RGB and thermal modalities provide complem

Hyperspectral image (HSI) is inevitably contaminated by mixed noise during acquisition and imaging, which severely affects subsequent applications. Therefore, HSI denoising is an indispensable part of

Point cloud compression often introduces noticeable reconstruction artifacts, which makes quality enhancement necessary. Existing approaches typically assume prior knowledge of the distortion level an