Search Results for author: Guangming Shi

Found 33 papers, 10 papers with code

Lightweight Image Super-Resolution with Hierarchical and Differentiable Neural Architecture Search

no code implementations9 May 2021 Han Huang, Li Shen, Chaoyang He, Weisheng Dong, HaoZhi Huang, Guangming Shi

Specifically, the cell-level search space is designed based on an information distillation mechanism, focusing on the combinations of lightweight operations and aiming to build a more lightweight and accurate SR structure.

Image Super-Resolution Neural Architecture Search +1

Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks

no code implementations21 Apr 2021 Chenxin Xu, Rong Xia, Yong Xiao, Yingyu Li, Guangming Shi, Kwang-cheng Chen

With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges.

Classification General Classification +1

PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network

1 code implementation12 Apr 2021 Pengfei Wang, Chengquan Zhang, Fei Qi, Shanshan Liu, Xiaoqiang Zhang, Pengyuan Lyu, Junyu Han, Jingtuo Liu, Errui Ding, Guangming Shi

With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency.

 Ranked #1 on Scene Text Detection on ICDAR 2015 (Accuracy metric)

Scene Text Detection Text Spotting

Searching Efficient Model-guided Deep Network for Image Denoising

no code implementations6 Apr 2021 Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, Leida Li, Guangming Shi

Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance.

Image Denoising Neural Architecture Search

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

1 code implementation CVPR 2021 Tao Huang, Weisheng Dong, Xin Yuan, Jinjian Wu, Guangming Shi

Different from existing GSM models using hand-crafted scale priors (e. g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN).

A new communication paradigm: from bit accuracy to semantic fidelity

no code implementations29 Jan 2021 Guangming Shi, Dahua Gao, Xiaodan Song, Jingxuan Chai, Minxi Yang, Xuemei Xie, Leida Li, Xuyang Li

In this article, we deploy semantics to solve the spectrum and power bottleneck and propose a first understanding and then transmission framework with high semantic fidelity.

Networking and Internet Architecture

Optical Flow Estimation via Motion Feature Recovery

no code implementations16 Jan 2021 Yang Jiao, Guangming Shi, Trac D. Tran

In this paper, we discover that the lost information is related to a large quantity of motion features (more than 40%) computed from the popular discriminative cost-volume feature would completely vanish due to invalid sampling, leading to the low efficiency of optical flow learning.

Optical Flow Estimation

Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment

1 code implementation ICCV 2021 Pengfei Chen, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences.

Unsupervised Domain Adaptation Video Quality Assessment +1

Temporal Graph Modeling for Skeleton-based Action Recognition

no code implementations16 Dec 2020 Jianan Li, Xuemei Xie, Zhifu Zhao, Yuhan Cao, Qingzhe Pan, Guangming Shi

Specifically, the constructed temporal relation graph explicitly builds connections between semantically related temporal features to model temporal relations between both adjacent and non-adjacent time steps.

Action Recognition Skeleton Based Action Recognition

Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks

no code implementations25 Nov 2020 Yong Xiao, Yingyu Li, Guangming Shi, H. Vincent Poor

The data uploading performance of IoT network and the computational capacity of edge servers are entangled with each other in influencing the FL model training process.

Federated Learning

EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation

no code implementations CVPR 2021 Yang Jiao, Trac D. Tran, Guangming Shi

This paper addresses the challenging unsupervised scene flow estimation problem by jointly learning four low-level vision sub-tasks: optical flow $\textbf{F}$, stereo-depth $\textbf{D}$, camera pose $\textbf{P}$ and motion segmentation $\textbf{S}$.

Depth Estimation Motion Segmentation +3

2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning

no code implementations16 Nov 2020 Yang Jiao, Yi Niu, Trac D. Tran, Guangming Shi

In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation.

3D Facial Expression Recognition

Towards Self-learning Edge Intelligence in 6G

no code implementations1 Oct 2020 Yong Xiao, Guangming Shi, Yingyu Li, Walid Saad, H. Vincent Poor

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.


A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition

no code implementations21 Sep 2020 Yang Li, Boxun Fu, Fu Li, Guangming Shi, Wenming Zheng

So it is necessary to give more attention to the EEG samples with strong transferability rather than forcefully training a classification model by all the samples.

EEG EEG Emotion Recognition

Towards Ubiquitous AI in 6G with Federated Learning

no code implementations26 Apr 2020 Yong Xiao, Guangming Shi, Marwan Krunz

One of the key challenges is the difficulty to implement distributed AI across a massive number of heterogeneous devices.

Federated Learning

MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment

1 code implementation CVPR 2020 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily.

Meta-Learning No-Reference Image Quality Assessment

Knowledge-guided Semantic Computing Network

no code implementations29 Sep 2018 Guangming Shi, Zhongqiang Zhang, Dahua Gao, Xuemei Xie, Yihao Feng, Xinrui Ma, Danhua Liu

Besides, to enhance the recognition ability of the semantic tree in aspects of the diversity, randomicity and variability, we use the traditional neural network to aid the semantic tree to learn some indescribable features.

Object Recognition

Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding

no code implementations18 Jul 2018 Fangfang Wu, Weisheng Dong, Guangming Shi, Xin Li

State-of-the-art approaches toward image restoration can be classified into model-based and learning-based.

Image Restoration

Joint Demosaicing and Denoising with Perceptual Optimization on a Generative Adversarial Network

no code implementations13 Feb 2018 Weishong Dong, Ming Yuan, Xin Li, Guangming Shi

Image demosaicing - one of the most important early stages in digital camera pipelines - addressed the problem of reconstructing a full-resolution image from so-called color-filter-arrays.

Demosaicking Denoising +1

Perceptual Compressive Sensing

1 code implementation1 Feb 2018 Jiang Du, Xuemei Xie, Chenye Wang, Guangming Shi

In detail, we employ perceptual loss, defined on feature level, to enhance the structure information of the recovered images.

Compressive Sensing

Full Image Recover for Block-Based Compressive Sensing

1 code implementation1 Feb 2018 Xuemei Xie, Chenye Wang, Jiang Du, Guangming Shi

In measurement part, the input image is adaptively measured block by block to acquire a group of measurements.

Compressive Sensing

ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning

no code implementations31 Jan 2018 Xiaotong Lu, Weisheng Dong, Peiyao Wang, Guangming Shi, Xuemei Xie

Instead of reconstructing individual blocks, the whole image is reconstructed from the linear convolutional measurements.

Compressive Sensing

Denoising Prior Driven Deep Neural Network for Image Restoration

no code implementations21 Jan 2018 Weisheng Dong, Peiyao Wang, Wotao Yin, Guangming Shi, Fangfang Wu, Xiaotong Lu

Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies.

Deblurring Image Denoising +2

Real-Time Illegal Parking Detection System Based on Deep Learning

no code implementations5 Oct 2017 Xuemei Xie, Chenye Wang, Shu Chen, Guangming Shi, Zhifu Zhao

Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.

Adaptive Measurement Network for CS Image Reconstruction

1 code implementation23 Sep 2017 Xuemei Xie, Yu-Xiang Wang, Guangming Shi, Chenye Wang, Jiang Du, Zhifu Zhao

In this paper, we propose an adaptive measurement network in which measurement is obtained by learning.

Compressive Sensing Image Reconstruction

Feature-Fused SSD: Fast Detection for Small Objects

1 code implementation15 Sep 2017 Guimei Cao, Xuemei Xie, Wenzhe Yang, Quan Liao, Guangming Shi, Jinjian Wu

We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects.

Small Object Detection

Learning Parametric Sparse Models for Image Super-Resolution

no code implementations NeurIPS 2016 Yongbo Li, Weisheng Dong, Xuemei Xie, Guangming Shi, Xin Li, Donglai Xu

More specifically, the parametric sparse prior of the desirable high-resolution (HR) image patches are learned from both the input low-resolution (LR) image and a training image dataset.

Image Super-Resolution

Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising

no code implementations ICCV 2015 Weisheng Dong, Guangyu Li, Guangming Shi, Xin Li, Yi Ma

Patch-based low-rank models have shown effective in exploiting spatial redundancy of natural images especially for the application of image denoising.

Dictionary Learning Image Denoising

Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding

no code implementations ICCV 2015 Yongbo Li, Weisheng Dong, Guangming Shi, Xuemei Xie

Existing approaches toward Image super-resolution (SR) is often either data-driven (e. g., based on internet-scale matching and web image retrieval) or model-based (e. g., formulated as an Maximizing a Posterior estimation problem).

Image Retrieval Image Super-Resolution +1

High-Speed Hyperspectral Video Acquisition With a Dual-Camera Architecture

no code implementations CVPR 2015 Lizhi Wang, Zhiwei Xiong, Dahua Gao, Guangming Shi, Wen-Jun Zeng, Feng Wu

We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution.

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