collaborators

13 papers

cs.CV2026

SC-Diff: Semantically Calibrated Diffusion for Visible-to-Infrared Image Translation

Junyin Zhang, Siyu Huang, Jianxiong Ye +4

Visible-to-infrared image translation provides a practical way to expand infrared training data using abundant visible images. Diffusion models are promising for this task because…

cs.CV2026

Registration-Grounded Spectral Fusion for Unregistered WLI/NBI Endoscopic Lesion Segmentation

Pengyu Jie, Wanquan Liu, Rui He +5

The paper proposes a reliability‑aware framework that first aligns features from white‑light and narrow‑band endoscopic images and then fuses them in a complex‑valued representatio…

cs.CV2026

Q-Fold: Query-Aware Focus-Context Spatio-Temporal Folding for Long Video Understanding

Biao Tang, Xu Chen, Shuxiang Gou +3

Long-video understanding remains challenging for multimodal large language models, because temporally extended videos often contain thousands of frames and are therefore expensive…

cs.CV2026

IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks

Yaming Zhang, Chenqiang Gao, Fangcen Liu +4

Existing infrared and visible (IR-VIS) methods inherit the general representations of Pre-trained Visual Models (PVMs) to facilitate complementary learning. However, our analysis i…

cs.CV2026

WildGHand: Learning Anti-Perturbation Gaussian Hand Avatars from Monocular In-the-Wild Videos

Hanhui Li, Xuan Huang, Wanquan Liu +5

Despite recent progress in 3D hand reconstruction from monocular videos, most existing methods rely on data captured in well-controlled environments and therefore degrade in real-w…

cs.CV2026

Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?

Chenqiang Gao, Chuandong Liu, Jun Shu +5

Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training. However, collecting such large-scale densely-s…