Search Results for author: Xiaohong Zhang

Found 12 papers, 4 papers with code

Rethinking the Sample Relations for Few-Shot Classification

1 code implementation23 Jan 2025 Guowei Yin, Sheng Huang, Luwen Huangfu, Yi Zhang, Xiaohong Zhang

MGRCL categorizes sample relations into three types: intra-sample relation of the same sample under different transformations, intra-class relation of homogenous samples, and inter-class relation of inhomogeneous samples.

Classification Contrastive Learning +4

DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference

no code implementations25 Apr 2024 Zhihao Shuai, Yinan Chen, Shunqiang Mao, Yihan Zho, Xiaohong Zhang

However, the accuracy of the segmentation results is often limited by insufficient supervision and the complex nature of medical imaging.

Decision Making Image Segmentation +3

LiDAR-Net: A Real-scanned 3D Point Cloud Dataset for Indoor Scenes

no code implementations CVPR 2024 Yanwen Guo, Yuanqi Li, Dayong Ren, Xiaohong Zhang, Jiawei Li, Liang Pu, Changfeng Ma, Xiaoyu Zhan, Jie Guo, Mingqiang Wei, Yan Zhang, Piaopiao Yu, Shuangyu Yang, Donghao Ji, Huisheng Ye, Hao Sun, Yansong Liu, Yinuo Chen, Jiaqi Zhu, Hongyu Liu

In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3. 6 billion precisely point-level annotated points covering an expansive area of 30000m^2.

Dual Latent State Learning: Exploiting Regional Network Similarities for QoS Prediction

no code implementations7 Oct 2023 Ziliang Wang, Xiaohong Zhang, Kechi Zhang, Ze Shi Li, Meng Yan

Individual objects, whether users or services, within a specific region often exhibit similar network states due to their shared origin from the same city or autonomous system (AS).

Prediction

Feature Noise Resilient for QoS Prediction with Probabilistic Deep Supervision

no code implementations3 Aug 2023 Ziliang Wang, Xiaohong Zhang, Ze Shi Li, Sheng Huang, Meng Yan

Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise.

Prediction Recommendation Systems

Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism

1 code implementation15 Mar 2023 Han Yang, Qiuli Wang, Yue Zhang, Zhulin An, Chen Liu, Xiaohong Zhang, S. Kevin Zhou

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations.

Lung Nodule Segmentation Segmentation

Regression-based Hypergraph Learning for Image Clustering and Classification

no code implementations14 Mar 2016 Sheng Huang, Dan Yang, Bo Liu, Xiaohong Zhang

Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph Transduction (RHT) models for addressing the image clustering and classification issues.

Classification Clustering +3

Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition

no code implementations28 Dec 2013 Sheng Huang, Dan Yang, Haopeng Zhang, Luwen Huangfu, Xiaohong Zhang

We further exploit the representational power of Haar wavelet and present a novel low-level face representation named Shape Primitives Histogram (SPH) for face recognition.

Face Recognition

Face Recognition via Globality-Locality Preserving Projections

no code implementations6 Nov 2013 Sheng Huang, Dan Yang, Fei Yang, Yongxin Ge, Xiaohong Zhang, Jiwen Lu

We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structures of data.

Face Recognition

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