109 citations · 358 across the 19 of their papers we have counts for
19 papers
CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network
Yudi Li, Min Tang, Yun Yang +5
We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network. The characters processed in our approach are not limite…
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Jiahua Dong, Guohua Cheng, Yue Zhang +5
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep…
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
Hongyi Wang, Luyang Luo, Fang Wang +5
Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSI classification is usually r…
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance
Hongyi Wang, Lanfen Lin, Hongjie Hu +6
High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3…
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise Perspective for Medical Image Segmentation
Huimin Huang, Shiao Xie1, Lanfen Lin +4
Recently, a variety of vision transformers have been developed as their capability of modeling long-range dependency. In current transformer-based backbones for medical image segme…
Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet and 2.5D Texture Transfer
Yinao Li, Yutaro Iwamoto, Ryousuke Nakamura +3
Recently, convolutional neural networks (CNN) have obtained promising results in single-image SR for hyperspectral pansharpening. However, enhancing CNNs' representation ability wi…
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation
Xu Ma, Junkun Yuan, Yen-wei Chen +2
Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a labeled source domain and adapts a trained model to an unlabeled target domain. To bridge the gap b…
N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks
Yudi Li, Min Tang, Yun Yang +5
We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by tr…
Mixed Transformer U-Net For Medical Image Segmentation
Hongyi Wang, Shiao Xie, Lanfen Lin +4
Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers h…
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
Yue Zhang, Chengtao Peng, Liying Peng +8
Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usuall…
Reconstructing Recognizable 3D Face Shapes based on 3D Morphable Models
Diqiong Jiang, Yiwei Jin, Fanglue Zhang +4
Many recent works have reconstructed distinctive 3D face shapes by aggregating shape parameters of the same identity and separating those of different people based on parametric mo…
Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
Huimin Huang, Ming Cai, Lanfen Lin +9
Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a mass of studies for lung infection segmentation from CT images. Though many methods have been proposed fo…
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images
Yingying Xu, Ming Cai, Lanfen Lin +10
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is new…
VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data
Yinhao Li, Yutaro Iwamoto, Lanfen Lin +2
Deep learning-based super-resolution (SR) techniques have generally achieved excellent performance in the computer vision field. Recently, it has been proven that three-dimensional…
P-Cloth: Interactive Complex Cloth Simulation on Multi-GPU Systems using Dynamic Matrix Assembly and Pipelined Implicit Integrators
Cheng Li, Min Tang, Ruofeng Tong +3
We present a novel parallel algorithm for cloth simulation that exploits multiple GPUs for fast computation and the handling of very high resolution meshes. To accelerate implicit…
Interactive Deep Refinement Network for Medical Image Segmentation
Titinunt Kitrungrotsakul, Iwamoto Yutaro, Lanfen Lin +3
Deep learning techniques have successfully been employed in numerous computer vision tasks including image segmentation. The techniques have also been applied to medical image segm…
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
Huimin Huang, Lanfen Lin, Ruofeng Tong +6
Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widel…
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
Chengyang Li, Dan Song, Ruofeng Tong +1
Multispectral pedestrian detection has attracted increasing attention from the research community due to its crucial competence for many around-the-clock applications (e.g., video…
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
Chengyang Li, Dan Song, Ruofeng Tong +1
Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. Ho…