7 papers
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…
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…
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…
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…
Diffusion-Guided Mask-Consistent Paired Mixing for Endoscopic Image Segmentation
Pengyu Jie, Wanquan Liu, Rui He +3
Augmentation for dense prediction typically relies on either sample mixing or generative synthesis. Mixing improves robustness but misaligned masks yield soft label ambiguity. Diff…
DPDETR: Decoupled Position Detection Transformer for Infrared-Visible Object Detection
Junjie Guo, Chenqiang Gao, Fangcen Liu +1
Infrared-visible object detection aims to achieve robust object detection by leveraging the complementary information of infrared and visible image pairs. However, the commonly exi…