Search Results for author: Huafeng Liu

Found 9 papers, 2 papers with code

Cluster-Wise Hierarchical Generative Model for Deep Amortized Clustering

no code implementations CVPR 2021 Huafeng Liu, Jiaqi Wang, Liping Jing

In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac).

Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones

1 code implementation23 Jan 2021 Huafeng Liu, Chuanyi Zhang, Yazhou Yao, Xiushen Wei, Fumin Shen, Jian Zhang, Zhenmin Tang

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.

Fine-Grained Visual Recognition

Interpretable Image Recognition by Constructing Transparent Embedding Space

1 code implementation ICCV 2021 Jiaqi Wang, Huafeng Liu, Xinyue Wang, Liping Jing

This plug-in embedding space is spanned by transparent basis concepts which are constructed on the Grassmann manifold.

Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network

no code implementations14 Sep 2020 Jianan Cui, Kuang Gong, Paul Han, Huafeng Liu, Quanzheng Li

After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.

SSIM Super-Resolution

Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network

no code implementations13 Sep 2020 Nuobei Xie, Kuang Gong, Ning Guo, Zhixing Qin, Jianan Cui, Zhifang Wu, Huafeng Liu, Quanzheng Li

Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.

Denoising

Deep Representation Learning for Road Detection through Siamese Network

no code implementations26 May 2019 Huafeng Liu, Xiaofeng Han, Xiangrui Li, Yazhou Yao, Pu Huang, Zhenming Tang

We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network.

Autonomous Driving Representation Learning

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