164 citations · 572 across the 17 of their papers we have counts for
6 papers · 1 filter
Learning a Single Convolutional Layer Model for Low Light Image Enhancement
Yuantong Zhang, Baoxin Teng, Daiqin Yang +4
Low-light image enhancement (LLIE) aims to improve the illuminance of images due to insufficient light exposure. Recently, various lightweight learning-based LLIE methods have been…
IterativePFN: True Iterative Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao +3
The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cl…
3DDesigner: Towards Photorealistic 3D Object Generation and Editing with Text-guided Diffusion Models
Gang Li, Heliang Zheng, Chaoyue Wang +3
Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we di…
PalQuant: Accelerating High-precision Networks on Low-precision Accelerators
Qinghao Hu, Gang Li, Qiman Wu +1
Recently low-precision deep learning accelerators (DLAs) have become popular due to their advantages in chip area and energy consumption, yet the low-precision quantized models on…
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +1
Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and o…
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Gang Li, Xiang Li, Yujie Wang +3
In this paper, we delve into two key techniques in Semi-Supervised Object Detection (SSOD), namely pseudo labeling and consistency training. We observe that these two techniques cu…