Search Results for author: Yalin Zheng

Found 11 papers, 7 papers with code

Regression of Instance Boundary by Aggregated CNN and GCN

no code implementations ECCV 2020 Yanda Meng, Wei Meng, Dongxu Gao, Yitian Zhao, Xiaoyun Yang, Xiaowei Huang, Yalin Zheng

In particular, thanks to the proposed aggregation GCN, our network benefits from direct feature learning of the instances’ boundary locations and the spatial information propagation across the image.

Semantic Segmentation

3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs

no code implementations9 Mar 2022 Yanda Meng, Xu Chen, Dongxu Gao, Yitian Zhao, Xiaoyun Yang, Yihong Qiao, Xiaowei Huang, Yalin Zheng

In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner.

3D Face Alignment 3D Face Reconstruction +1

Counting with Adaptive Auxiliary Learning

1 code implementation8 Mar 2022 Yanda Meng, Joshua Bridge, Meng Wei, Yitian Zhao, Yihong Qiao, Xiaoyun Yang, Xiaowei Huang, Yalin Zheng

This paper proposes an adaptive auxiliary task learning based approach for object counting problems.

Auxiliary Learning Object Counting

BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation

1 code implementation27 Oct 2021 Yanda Meng, Hongrun Zhang, Dongxu Gao, Yitian Zhao, Xiaoyun Yang, Xuesheng Qian, Xiaowei Huang, Yalin Zheng

Our model is well-suited to obtain global semantic region information while also accommodates local spatial boundary characteristics simultaneously.

Semantic Segmentation

Spatial Uncertainty-Aware Semi-Supervised Crowd Counting

1 code implementation ICCV 2021 Yanda Meng, Hongrun Zhang, Yitian Zhao, Xiaoyun Yang, Xuesheng Qian, Xiaowei Huang, Yalin Zheng

Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations.

Crowd Counting

3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation

no code implementations26 Feb 2021 Shuai Yu, Jianyang Xie, Jinkui Hao, Yalin Zheng, Jiong Zhang, Yan Hu, Jiang Liu, Yitian Zhao

Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysis

Decision Making Depth Estimation +1

Learning Euler's Elastica Model for Medical Image Segmentation

1 code implementation1 Nov 2020 Xu Chen, Xiangde Luo, Yitian Zhao, Shaoting Zhang, Guotai Wang, Yalin Zheng

Inspired by Euler's Elastica model and recent active contour models introduced into the field of deep learning, we propose a novel active contour with elastica (ACE) loss function incorporating Elastica (curvature and length) and region information as geometrically-natural constraints for the image segmentation tasks.

Medical Image Segmentation Semantic Segmentation

CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

1 code implementation15 Oct 2020 Lei Mou, Yitian Zhao, Huazhu Fu, Yonghuai Liu, Jun Cheng, Yalin Zheng, Pan Su, Jianlong Yang, Li Chen, Alejandro F Frang, Masahiro Akiba, Jiang Liu

Automated detection of curvilinear structures, e. g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases.

ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

1 code implementation10 Jul 2020 Yuhui Ma, Huaying Hao, Huazhu Fu, Jiong Zhang, Jianlong Yang, Jiang Liu, Yalin Zheng, Yitian Zhao

To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level.

Development and Validation of a Novel Prognostic Model for Predicting AMD Progression Using Longitudinal Fundus Images

no code implementations10 Jul 2020 Joshua Bridge, Simon P. Harding, Yalin Zheng

We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals, which requires no prior feature extraction.

Cannot find the paper you are looking for? You can Submit a new open access paper.