Search Results for author: Xian-Ming Liu

Found 20 papers, 5 papers with code

AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation

no code implementations16 Aug 2020 Xinyu Gong, Wuyang Chen, Yifan Jiang, Ye Yuan, Xian-Ming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations.

Neural Architecture Search Pose Estimation

Graph Signal Processing for Geometric Data and Beyond: Theory and Applications

no code implementations5 Aug 2020 Wei Hu, Jiahao Pang, Xian-Ming Liu, Dong Tian, Chia-Wen Lin, Anthony Vetro

Geometric data acquired from real-world scenes, e. g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc.

Autonomous Driving

Single Image Deraining via Scale-space Invariant Attention Neural Network

no code implementations9 Jun 2020 Bo Pang, Deming Zhai, Junjun Jiang, Xian-Ming Liu

Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems.

Image Enhancement Single Image Deraining

Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery

2 code implementations18 May 2020 Junjun Jiang, He Sun, Xian-Ming Liu, Jiayi Ma

Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs).

Hyperspectral Image Super-Resolution Image Super-Resolution

Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis

no code implementations17 Mar 2020 Ruifeng Shi, Deming Zhai, Xian-Ming Liu, Junjun Jiang, Wen Gao

However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation.

General Classification Image Classification +1

ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising

no code implementations4 Mar 2020 Yongsen Zhao, Deming Zhai, Junjun Jiang, Xian-Ming Liu

Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation.

Hyperspectral Image Denoising Image Denoising

FasterSeg: Searching for Faster Real-time Semantic Segmentation

1 code implementation ICLR 2020 Wuyang Chen, Xinyu Gong, Xian-Ming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods.

Neural Architecture Search Real-Time Semantic Segmentation

Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network

1 code implementation2 Aug 2019 Tao Hu, Lichao Huang, Xian-Ming Liu, Han Shen

Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical.

Motion Estimation Real-Time Visual Tracking

Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior

no code implementations11 Jun 2019 Yuanchao Bai, Huizhu Jia, Ming Jiang, Xian-Ming Liu, Xiaodong Xie, Wen Gao

Blind image deblurring is a challenging problem in computer vision, which aims to restore both the blur kernel and the latent sharp image from only a blurry observation.

Blind Image Deblurring Image Deblurring +3

Hyperspectral Image Classification in the Presence of Noisy Labels

1 code implementation12 Sep 2018 Junjun Jiang, Jiayi Ma, Zheng Wang, Chen Chen, Xian-Ming Liu

The key idea of RLPA is to exploit knowledge (e. g., the superpixel based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation.

General Classification Hyperspectral Image Classification

Connecting Image Denoising and High-Level Vision Tasks via Deep Learning

1 code implementation6 Sep 2018 Ding Liu, Bihan Wen, Jianbo Jiao, Xian-Ming Liu, Zhangyang Wang, Thomas S. Huang

Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation.

Image Denoising

Graph-Based Blind Image Deblurring From a Single Photograph

no code implementations22 Feb 2018 Yuanchao Bai, Gene Cheung, Xian-Ming Liu, Wen Gao

We leverage the new graph spectral interpretation for RGTV to design an efficient algorithm that solves for the skeleton image and the blur kernel alternately.

Blind Image Deblurring Image Deblurring

Blind Image Deblurring via Reweighted Graph Total Variation

no code implementations24 Dec 2017 Yuanchao Bai, Gene Cheung, Xian-Ming Liu, Wen Gao

The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, de-convolve blurry input to restore the target image.

Blind Image Deblurring Image Deblurring

Mapping the world population one building at a time

no code implementations15 Dec 2017 Tobias G. Tiecke, Xian-Ming Liu, Amy Zhang, Andreas Gros, Nan Li, Gregory Yetman, Talip Kilic, Siobhan Murray, Brian Blankespoor, Espen B. Prydz, Hai-Anh H. Dang

Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale.

Disaster Response

Robust Video Super-Resolution With Learned Temporal Dynamics

no code implementations ICCV 2017 Ding Liu, Zhaowen Wang, Yuchen Fan, Xian-Ming Liu, Zhangyang Wang, Shiyu Chang, Thomas Huang

Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network that is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner.

Video Super-Resolution

Building Detection from Satellite Images on a Global Scale

no code implementations27 Jul 2017 Amy Zhang, Xian-Ming Liu, Andreas Gros, Tobias Tiecke

Our work is some of the first to create population density maps from building detection on a large scale.

Density Estimation

Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks

no code implementations ICCV 2015 Chunshui Cao, Xian-Ming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, Deva Ramanan, Thomas S. Huang

While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections.

Understanding Image Structure via Hierarchical Shape Parsing

no code implementations CVPR 2015 Xian-Ming Liu, Rongrong Ji, Changhu Wang, Wei Liu, Bineng Zhong, Thomas S. Huang

A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space.

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