Search Results for author: Guangming Shi

Found 64 papers, 20 papers with code

AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception

1 code implementation15 Apr 2024 Yipo Huang, Xiangfei Sheng, Zhichao Yang, Quan Yuan, Zhichao Duan, Pengfei Chen, Leida Li, Weisi Lin, Guangming Shi

To address the above challenge, we first introduce a comprehensively annotated Aesthetic Multi-Modality Instruction Tuning (AesMMIT) dataset, which serves as the footstone for building multi-modality aesthetics foundation models.

Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement

no code implementations14 Feb 2024 Huachen Fang, Jinjian Wu, Qibin Hou, Weisheng Dong, Guangming Shi

Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs.


Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining

1 code implementation5 Feb 2024 Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Yizhou Yu, Yong Liang, Guangming Shi, Shaoting Zhang, Hairong Zheng, Shanshan Wang

However, it is challenging for existing methods to model long-range global information, where convolutional neural networks (CNNs) are constrained by their local receptive fields, and vision transformers (ViTs) suffer from high quadratic complexity of their attention mechanism.

Image Segmentation Medical Image Segmentation +1

PACE: A Pragmatic Agent for Enhancing Communication Efficiency Using Large Language Models

no code implementations30 Jan 2024 Jiaxuan Li, Minxi Yang, Dahua Gao, Wenlong Xu, Guangming Shi

This paper proposes an image pragmatic communication framework based on a Pragmatic Agent for Communication Efficiency (PACE) using Large Language Models (LLM).

Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration

no code implementations23 Jan 2024 Yifan Zhang, Siyu Ren, Junhui Hou, Jinjian Wu, Guangming Shi

First, we propose the learnable transformation alignment to bridge the domain gap between image and point cloud data, converting features into a unified representation space for effective comparison and matching.

3D Semantic Segmentation Autonomous Driving +4

Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques

no code implementations3 Jan 2024 Weijian Huang, Cheng Li, Hong-Yu Zhou, Jiarun Liu, Hao Yang, Yong Liang, Guangming Shi, Hairong Zheng, Shanshan Wang

The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications.

Representation Learning

Segment Any Events via Weighted Adaptation of Pivotal Tokens

1 code implementation24 Dec 2023 Zhiwen Chen, Zhiyu Zhu, Yifan Zhang, Junhui Hou, Guangming Shi, Jinjian Wu

One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery.

Event-based Object Segmentation

Rate-Distortion-Perception Theory for Semantic Communication

no code implementations9 Dec 2023 Jingxuan Chai, Yong Xiao, Guangming Shi, Walid Saad

Motivated by the fact that the semantic information generally involves rich intrinsic knowledge that cannot always be directly observed by the encoder, we consider a semantic information source that can only be indirectly sensed by the encoder.

Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing

no code implementations8 Dec 2023 Huixiang Zhu, Yong Xiao, Yingyu Li, Guangming Shi, Walid Saad

Motivated by the observation that signals recorded by wireless receivers are closely related to a set of physical-layer semantic features, in this paper we propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.

Gesture Recognition Zero-Shot Learning

Degradation Estimation Recurrent Neural Network with Local and Non-Local Priors for Compressive Spectral Imaging

1 code implementation15 Nov 2023 Yubo Dong, Dahua Gao, Yuyan Li, Guangming Shi, Danhua Liu

In the Coded Aperture Snapshot Spectral Imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3D hyperspectral images (HSIs) from 2D measurements.

Retinex-guided Channel-grouping based Patch Swap for Arbitrary Style Transfer

no code implementations19 Sep 2023 Chang Liu, Yi Niu, Mingming Ma, Fu Li, Guangming Shi

The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps.

Patch Matching Style Transfer

EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition

no code implementations9 Aug 2023 Yijin Zhou, Fu Li, Yang Li, Youshuo Ji, Lijian Zhang, Yuanfang Chen, Wenming Zheng, Guangming Shi

The transfer module encodes the domain-specific information of source and target domains and then re-constructs the source domain's emotional pattern and the target domain's statistical characteristics into the new stylized EEG representations.

EEG EEG Emotion Recognition +1

Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework

1 code implementation20 Jun 2023 Yong Xiao, Yiwei Liao, Yingyu Li, Guangming Shi, H. Vincent Poor, Walid Saad, Merouane Debbah, Mehdi Bennis

Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal.

Imitation Learning

Mathematical Characterization of Signal Semantics and Rethinking of the Mathematical Theory of Information

no code implementations26 Mar 2023 Guangming Shi, Dahua Gao, Shuai Ma, Minxi Yang, Yong Xiao, Xuemei Xie

Shannon information theory is established based on probability and bits, and the communication technology based on this theory realizes the information age.

Features Disentangled Semantic Broadcast Communication Networks

no code implementations3 Mar 2023 Shuai Ma, Weining Qiao, Youlong Wu, Hang Li, Guangming Shi, Dahua Gao, Yuanming Shi, Shiyin Li, Naofal Al-Dhahir

Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users' intended semantic features are selected for broadcasting, which can further improve the transmission efficiency.

feature selection

Task-oriented Explainable Semantic Communications

no code implementations27 Feb 2023 Shuai Ma, Weining Qiao, Youlong Wu, Hang Li, Guangming Shi, Dahua Gao, Yuanming Shi, Shiyin Li, Naofal Al-Dhahir

Furthermore, based on the $\beta $-variational autoencoder ($\beta $-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise.

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach

no code implementations1 Feb 2023 Yong Xiao, Rong Xia, Yingyu Li, Guangming Shi, Diep N. Nguyen, Dinh Thai Hoang, Dusit Niyato, Marwan Krunz

FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset.

Federated Learning Self-Supervised Learning

Adversarial Learning for Implicit Semantic-Aware Communications

no code implementations27 Jan 2023 Zhimin Lu, Yong Xiao, Zijian Sun, Yingyu Li, Guangming Shi, Xianfu Chen, Mehdi Bennis, H. Vincent Poor

In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user.

Time-sensitive Learning for Heterogeneous Federated Edge Intelligence

no code implementations26 Jan 2023 Yong Xiao, Xiaohan Zhang, Guangming Shi, Marwan Krunz, Diep N. Nguyen, Dinh Thai Hoang

A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, local epoch number.

Decision Making Edge-computing +1

Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring

no code implementations CVPR 2023 Zhenxuan Fang, Fangfang Wu, Weisheng Dong, Xin Li, Jinjian Wu, Guangming Shi

To address these issues, we propose to represent the field of motion blur kernels in a latent space by normalizing flows, and design CNNs to predict the latent codes instead of motion kernels.

Blind Image Deblurring Image Deblurring

Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware Attention

1 code implementation ICCV 2023 Yunlong Liu, Tao Huang, Weisheng Dong, Fangfang Wu, Xin Li, Guangming Shi

Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods.

Low-Light Image Enhancement Quantization

Vector Quantization With Self-Attention for Quality-Independent Representation Learning

no code implementations CVPR 2023 Zhou Yang, Weisheng Dong, Xin Li, Mengluan Huang, Yulin Sun, Guangming Shi

During training, we enforce the quantization of features from clean and corrupted images in the same discrete embedding space so that an invariant quality-independent feature representation can be learned to improve the recognition robustness of low-quality images.

Data Augmentation Image Restoration +2

Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging

1 code implementation CVPR 2023 Yubo Dong, Dahua Gao, Tian Qiu, Yuyan Li, Minxi Yang, Guangming Shi

However, in the data subproblem, the used sensing matrix is ill-suited for the real degradation process due to the device errors caused by phase aberration, distortion; in the prior subproblem, it is important to design a suitable model to jointly exploit both spatial and spectral priors.

Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning

1 code implementation28 Oct 2022 Yong Xiao, Zijian Sun, Guangming Shi, Dusit Niyato

A federated GCN-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized knowledge datasets.

Imitation Learning

Ecsnet: Spatio-temporal feature learning for event camera

1 code implementation IEEE Transactions on Circuits and Systems for Video Technology 2022 Zhiwen Chen, Jinjian Wu, Junhui Hou, Leida Li, Weisheng Dong, Guangming Shi

To fully exploit their inherent sparsity with reconciling the spatio-temporal information, we introduce a compact event representation, namely 2D-1T event cloud sequence (2D-1T ECS).

Action Recognition Event-based vision +2

Learning Primitive-aware Discriminative Representations for Few-shot Learning

no code implementations20 Aug 2022 Jianpeng Yang, Yuhang Niu, Xuemei Xie, Guangming Shi

To fur-ther enhance the discriminability and transferability of primitives, we propose a visual primitive Correlation Reasoning Network (CRN) based on graph convolu-tional network to learn abundant structural information and internal correlation among primitives.

Few-Shot Learning

GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition

1 code implementation12 Apr 2022 Yang Li, Ji Chen, Fu Li, Boxun Fu, Hao Wu, Youshuo Ji, Yijin Zhou, Yi Niu, Guangming Shi, Wenming Zheng

GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks.

Contrastive Learning EEG +2

ModulE: Module Embedding for Knowledge Graphs

no code implementations9 Mar 2022 Jingxuan Chai, Guangming Shi

Specifically, under our framework, we introduce a more generic embedding method, ModulE, which projects entities to a module.

Knowledge Graph Embedding Knowledge Graphs

Robust Depth Completion with Uncertainty-Driven Loss Functions

no code implementations15 Dec 2021 Yufan Zhu, Weisheng Dong, Leida Li, Jinjian Wu, Xin Li, Guangming Shi

In this work, we introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion.

Depth Completion

Progressive Graph Convolution Network for EEG Emotion Recognition

no code implementations14 Dec 2021 Yijin Zhou, Fu Li, Yang Li, Youshuo Ji, Guangming Shi, Wenming Zheng, Lijian Zhang, Yuanfang Chen, Rui Cheng

Moreover, motivated by the observation of the relationship between coarse- and fine-grained emotions, we adopt a dual-head module that enables the PGCN to progressively learn more discriminative EEG features, from coarse-grained (easy) to fine-grained categories (difficult), referring to the hierarchical characteristic of emotion.

EEG EEG Emotion Recognition

Uncertainty-Driven Loss for Single Image Super-Resolution

no code implementations NeurIPS 2021 Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, Guangming Shi

Specifically, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis into SISR solutions so the targeted pixels in a high-resolution image (mean) and their corresponding uncertainty (variance) can be learned simultaneously.

Image Super-Resolution

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

2 code implementations12 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)

Decoder Optical Character Recognition (OCR) +2

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 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.

Edge-computing Self-Learning

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 +1

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.

Adversarial Robustness 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 +2

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

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

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.

Blocking 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.

object-detection 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

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 +2

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

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.

Vocal Bursts Intensity Prediction

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