726 citations · 1.1k across the 28 of their papers we have counts for
28 papers
RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments
Jiahui Zhang, Shitao Tang, Kejie Qiu +6
Visual relocalization has been a widely discussed problem in 3D vision: given a pre-constructed 3D visual map, the 6 DoF (Degrees-of-Freedom) pose of a query image is estimated. Re…
Denoised Non-Local Neural Network for Semantic Segmentation
Qi Song, Jie Li, Hao Guo +1
The non-local network has become a widely used technique for semantic segmentation, which computes an attention map to measure the relationships of each pixel pair. However, most o…
PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation
Hualie Jiang, Laiyan Ding, Junjie Hu +1
Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions. Fortunately, the indoor scenes are full…
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Rui Huang, Andrew Geng, Yixuan Li
Detecting out-of-distribution (OOD) data has become a critical component in ensuring the safe deployment of machine learning models in the real world. Existing OOD detection approa…
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation
Ran Gu, Jingyang Zhang, Rui Huang +3
Domain generalizable model is attracting increasing attention in medical image analysis since data is commonly acquired from different institutes with various imaging protocols and…
Unsupervised Monocular Depth Perception: Focusing on Moving Objects
Hualie Jiang, Laiyan Ding, Zhenglong Sun +1
As a flexible passive 3D sensing means, unsupervised learning of depth from monocular videos is becoming an important research topic. It utilizes the photometric errors between the…
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement
Jie Li, Laiyan Ding, Rui Huang
3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same sem…
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition
Yulin Wang, Rui Huang, Shiji Song +2
Vision Transformers (ViT) have achieved remarkable success in large-scale image recognition. They split every 2D image into a fixed number of patches, each of which is treated as a…
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
Yunhe Gao, Rui Huang, Yiwei Yang +7
Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid…
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom
Kangfu Mei, Shenglong Ye, Rui Huang
Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images. However, these algorithms often yield significant artifacts w…
Learning Camera Localization via Dense Scene Matching
Shitao Tang, Chengzhou Tang, Rui Huang +2
Camera localization aims to estimate 6 DoF camera poses from RGB images. Traditional methods detect and match interest points between a query image and a pre-built 3D model. Recent…
AR Mapping: Accurate and Efficient Mapping for Augmented Reality
Rui Huang, Chuan Fang, Kejie Qiu +4
Augmented reality (AR) has gained increasingly attention from both research and industry communities. By overlaying digital information and content onto the physical world, AR enab…
Compact 3D Map-Based Monocular Localization Using Semantic Edge Alignment
Kejie Qiu, Shenzhou Chen, Jiahui Zhang +4
Accurate localization is fundamental to a variety of applications, such as navigation, robotics, autonomous driving, and Augmented Reality (AR). Different from incremental localiza…
AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing
Qi Song, Kangfu Mei, Rui Huang
Two factors have proven to be very important to the performance of semantic segmentation models: global context and multi-level semantics. However, generating features that capture…
Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss
Wenhui Lei, Haochen Mei, Zhengwentai Sun +7
Nasopharyngeal Carcinoma (NPC) is a leading form of Head-and-Neck (HAN) cancer in the Arctic, China, Southeast Asia, and the Middle East/North Africa. Accurate segmentation of Orga…
UniFuse: Unidirectional Fusion for 360 Panorama Depth Estimation
Hualie Jiang, Zhe Sheng, Siyu Zhu +2
Learning depth from spherical panoramas is becoming a popular research topic because a panorama has a full field-of-view of the environment and provides a relatively complete descr…
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
Xu Yan, Jiantao Gao, Jie Li +4
LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep…
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Yulin Wang, Kangchen Lv, Rui Huang +3
The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images. However, this often comes at a high computational cost and hig…
Concentrated Multi-Grained Multi-Attention Network for Video Based Person Re-Identification
Panwen Hu, Jiazhen Liu, Rui Huang
Occlusion is still a severe problem in the video-based Re-IDentification (Re-ID) task, which has a great impact on the success rate. The attention mechanism has been proved to be h…
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Ran Gu, Guotai Wang, Tao Song +6
Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance f…
Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets
Rui Huang, Yuanjie Zheng, Zhiqiang Hu +2
Multi-organ segmentation has extensive applications in many clinical applications. To segment multiple organs of interest, it is generally quite difficult to collect full annotatio…
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds
Rui Huang, Wanyue Zhang, Abhijit Kundu +4
Detecting objects in 3D LiDAR data is a core technology for autonomous driving and other robotics applications. Although LiDAR data is acquired over time, most of the 3D object det…
Disentangle Perceptual Learning through Online Contrastive Learning
Kangfu Mei, Yao Lu, Qiaosi Yi +3
Pursuing realistic results according to human visual perception is the central concern in the image transformation tasks. Perceptual learning approaches like perceptual loss are em…
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos
Hualie Jiang, Laiyan Ding, Zhenglong Sun +1
Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised…