1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- City University of Hong KongHK108 papers
- University of Hong KongHK37 papers
- Shandong UniversityCN27 papers
- Hong Kong University of Science and TechnologyHK20 papers
- Chinese Academy of SciencesCN17 papers
- Wuhan UniversityCN14 papers
- Peking UniversityCN11 papers
- University of Chinese Academy of SciencesCN10 papers
- Suzhou Research InstituteCN9 papers
- Southern University of Science and TechnologyCN8 papers
- Tianjin UniversityCN8 papers
- Institute of PhysicsPL7 papers
12 papers · 1 filter
VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations
Yuexiong Ding, Xiaowei Luo
Currently, object detection applications in construction are almost based on pure 2D data (both image and annotation are 2D-based), resulting in the developed artificial intelligen…
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation
Huimin Wu, Xiaomeng Li, Yiqun Lin +1
This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the exist…
Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement
Baoliang Chen, Lingyu Zhu, Hanwei Zhu +3
There is a growing consensus in the research community that the optimization of low-light image enhancement approaches should be guided by the visual quality perceived by end users…
CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
Siyu Ren, Yiming Zeng, Junhui Hou +1
Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the fir…
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution
Jinhui Hou, Zhiyu Zhu, Junhui Hou +3
In this paper, we investigate the problem of hyperspectral (HS) image spatial super-resolution via deep learning. Particularly, we focus on how to embed the high-dimensional spatia…
RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images
Runmin Cong, Yumo Zhang, Leyuan Fang +3
Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some sal…
Visual-Tactile Cross-Modal Data Generation using Residue-Fusion GAN with Feature-Matching and Perceptual Losses
Shaoyu Cai, Kening Zhu, Yuki Ban +1
Existing psychophysical studies have revealed that the cross-modal visual-tactile perception is common for humans performing daily activities. However, it is still challenging to b…
Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network
Zhangkai Ni, Wenhan Yang, Shiqi Wang +2
Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to lea…
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Qijian Zhang, Runmin Cong, Chongyi Li +5
Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains a…
Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images
Chongyi Li, Runmin Cong, Junhui Hou +3
Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend…
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
Qingnan Fan, Jiaolong Yang, Gang Hua +2
This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering.…
Revisiting Deep Intrinsic Image Decompositions
Qingnan Fan, Jiaolong Yang, Gang Hua +2
While invaluable for many computer vision applications, decomposing a natural image into intrinsic reflectance and shading layers represents a challenging, underdetermined inverse…