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A Theory for Semantic Channel Coding With Many-to-one Source
Shuai Ma;Chuanhui Zhang;Huayan Qi;Hang Li;Yue Bi;Guangming Shi;Naofal Al-Dhahir
IEEE Transactions on Cognitive Communications and Networking
Year: 2025 | Early Access Article |
As one of the potential key technologies of 6G, semantic communication is still in its infancy and there are many open problems, such as semantic entropy definition and semantic channel coding theory. To address these challenges, we investigate semantic information measures and semantic channel coding theorem. Specifically, we propose a semantic entropy definition as the uncertainty in the semanti...Show More
Visual Environment-Interactive Planning for Embodied Complex-Question Answering
IEEE Transactions on Circuits and Systems for Video Technology
Year: 2025 | Early Access Article |
This study focuses on Embodied Complex-Question Answering task, which means the embodied robot need to understand human questions with intricate structures and abstract semantics. The core of this task lies in making appropriate plans based on the perception of the visual environment. Existing methods often generate plans in a once-for-all manner, i.e., one-step planning. Such approach rely on lar...Show More
Joint Spatial and Frequency Domain Learning for Lightweight Spectral Image Demosaicing
Fangfang Wu;Tao Huang;Junwei Xu;Xun Cao;Weisheng Dong;Le Dong;Guangming Shi
Conventional spectral image demosaicing algorithms rely on pixels’ spatial or spectral correlations for reconstruction. Due to the missing data in the multispectral filter array (MSFA), the estimation of spatial or spectral correlations is inaccurate, leading to poor reconstruction results, and these algorithms are time-consuming. Deep learning-based spectral image demosaicing methods directly lea...Show More
Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels
Shuai Ma;Zhiye Sun;Bin Shen;Youlong Wu;Hang Li;Guangming Shi;Shiyin Li;Naofal Al-Dhahir
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in...Show More
Local Uncertainty Energy Transfer for Active Domain Adaptation
Yulin Sun;Guangming Shi;Weisheng Dong;Xin Li;Le Dong;Xuemei Xie
Active Domain Adaptation (ADA) improves knowledge transfer efficiency from the labeled source domain to the unlabeled target domain by selecting a few target sample labels. However, most existing active sampling methods ignore the local uncertainty of neighbors in the target domain, making it easier to pick out anomalous samples that are detrimental to the model. To address this problem, we presen...Show More
Distributed Optimization of Resource-Efficiency for Federated Edge Intelligence in UAV-Enabled IoT Networks
Yingyu Li;Jiangying Rao;Lian Wang;Yong Xiao;Xiaohu Ge;Guangming Shi
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
UAV-enabled IoT networks have shown promising potential in a range of novel applications and service scenarios such as extending the network coverage, extending the battery lifetime of IoT networks, and also supporting temporary data collecting and processing needs in various emergency situations. This paper studies a federated edge intelligence (FEI) network based on the data collected and upload...Show More
CDS-Net: Contextual Difference Sensitivity Network for Pixel-Wise Road Crack Detection
Qinzhong Tan;Ao Li;Le Dong;Weisheng Dong;Xin Li;Guangming Shi
IEEE Transactions on Circuits and Systems for Video Technology
Year: 2025 | Early Access Article |
Road crack detection is a key computer vision task that identifies and locates cracks in road surface images, which usually have an irregular shape and contain only a few pixels in width. Generative and unsupervised methods are popular these years, but generative methods require a lot of training data and computational power while unsupervised methods are not so satisfactory in pixel-level segment...Show More
Spatial-Spectral Mixing Transformer With Hybrid Image Prior for Multispectral Image Demosaicing
Le Dong;Mengzu Liu;Tengteng Tang;Tao Huang;Jie Lin;Weisheng Dong;Guangming Shi
The snapshot multispectral imaging system using a multispectral filter array (MSFA) efficiently captures sample the multispectral image (MSI) information of scenes and obtain spectral mosaic images. To obtain the complete MSI information from these spectral mosaics, effective demosaicing methods are essential. Traditional MSI demosaicing techniques depend on pixel correlation and various hand-craf...Show More
CrossEI: Boosting Motion-Oriented Object Tracking With an Event Camera
With the differential sensitivity and high time resolution, event cameras can record detailed motion clues, which form a complementary advantage with frame-based cameras to enhance the object tracking, especially in challenging dynamic scenes. However, how to better match heterogeneous event-image data and exploit rich complementary cues from them still remains an open issue. In this paper, we ali...Show More
Scene Prior Constrained Self-Paced Learning for Unsupervised Satellite Video Vehicle Detection
Recently, deep learning has significantly advanced the satellite object detection. However, the effectiveness of these methods heavily relies on abundant and accurate annotations, which are extremely labor-intensive for satellite videos. Meanwhile, the robustness of traditional difference-based methods is limited by the hand-craft feature from the satellite videos with low-resolution and frame mis...Show More
Multi-Modality Multi-Attribute Contrastive Pre-Training for Image Aesthetics Computing
Yipo Huang;Leida Li;Pengfei Chen;Haoning Wu;Weisi Lin;Guangming Shi
In the Image Aesthetics Computing (IAC) field, most prior methods leveraged the off-the-shelf backbones pre-trained on the large-scale ImageNet database. While these pre-trained backbones have achieved notable success, they often overemphasize object-level semantics and fail to capture the high-level concepts of image aesthetics, which may only achieve suboptimal performances. To tackle this long-...Show More
SANSee: A Physical-Layer Semantic-Aware Networking Framework for Distributed Wireless Sensing
Contactless device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications using ubiquitously available radio frequency (RF) signals. Traditional approaches focus on developing a single global model based on a combined dataset collected from different locations. However, wireless signals are ...Show More
Fast Window-Based Event Denoising With Spatiotemporal Correlation Enhancement
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. In this paper, we propose window-based event denoising, which simultaneously deals with a stack of events while existing element-based denoising focuses on one event each time. Besides, we give the theoretical analysis based...Show More
Learning Frame-Event Fusion for Motion Deblurring
Wen Yang;Jinjian Wu;Jupo Ma;Leida Li;Weisheng Dong;Guangming Shi
Motion deblurring is a highly ill-posed problem due to the significant loss of motion information in the blurring process. Complementary informative features from auxiliary sensors such as event cameras can be explored for guiding motion deblurring. The event camera can capture rich motion information asynchronously with microsecond accuracy. In this paper, a novel frame-event fusion framework is ...Show More
Multi-Scale Spatio-Temporal Memory Network for Lightweight Video Denoising
Lu Sun;Fangfang Wu;Wei Ding;Xin Li;Jie Lin;Weisheng Dong;Guangming Shi
Deep learning-based video denoising methods have achieved great performance improvements in recent years. However, the expensive computational cost arising from sophisticated network design has severely limited their applications in real-world scenarios. To address this practical weakness, we propose a multiscale spatio-temporal memory network for fast video denoising, named MSTMN, aiming at strik...Show More
Swin Kansformer-Based Semantic Communication Systems for Wireless Image Transmission
2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops)
Year: 2024 | Conference Paper |
With the continuous advancement of wireless communication technology, numerous applications are gaining increasing popularity, driving the quest for more efficient and innovative semantic communication frameworks. In this paper, we investigate a semantic communication architecture specifically designed for wireless image transmission. We redesign Swin Transformer (SwinT) and propose a new architec...Show More
Intellicise Wireless Networks From Semantic Communications: A Survey, Research Issues, and Challenges
Ping Zhang;Wenjun Xu;Yiming Liu;Xiaoqi Qin;Kai Niu;Shuguang Cui;Guangming Shi;Zhijin Qin;Xiaodong Xu;Fengyu Wang;Yue Meng;Chen Dong;Jincheng Dai;Qianqian Yang;Yaping Sun;Dahua Gao;Hui Gao;Shujun Han;Xiaodan Song
IEEE Communications Surveys & Tutorials
Year: 2024 | Early Access Article |
Information and communication technology (ICT) has been an essential part of modern society. However, the current communication systems are not sufficient to meet the demands of emerging applications. Intellicise (intelligent and concise) wireless networks, with their inherent characteristics of intelligence-endogenous and primitive-concise, have been proposed as a promising research direction. In...Show More
Intellicise Wireless Networks From Semantic Communications: A Survey, Research Issues, and Challenges
Ping Zhang;Wenjun Xu;Yiming Liu;Xiaoqi Qin;Kai Niu;Shuguang Cui;Guangming Shi;Zhijin Qin;Xiaodong Xu;Fengyu Wang;Yue Meng;Chen Dong;Jincheng Dai;Qianqian Yang;Yaping Sun;Dahua Gao;Hui Gao;Shujun Han;Xiaodan Song
Year: 2024 | Early Access Article |
A Comprehensive Review of RGBT Tracking
Haiping Zhang;Di Yuan;Xiu Shu;Zhihui Li;Qiao Liu;Xiaojun Chang;Zhenyu He;Guangming Shi
RGBT tracking seeks to leverage both visible and thermal infrared images to enhance the robustness of target tracking. This method makes up for the limitations of single-sensor tracking. The RGB and thermal infrared images complement each other effectively, enabling the tracker to operate seamlessly in complex environments day and night. However, RGBT tracking still faces challenges, such as therm...Show More
Human Pose Estimation via Parse Graph of Body Structure
When observing a person’s body, humans can extract the structured representation of the body called a parse graph, which includes the hierarchical decompositions from the entire body to parts and primitives and the context relations by horizontal links between the body parts. This ability helps humans better locate body structures at different levels. In order for the model to have this ability fo...Show More
Semantic Feature Division Multiple Access for Multi-User Digital Interference Networks
Shuai Ma;Chuanhui Zhang;Bin Shen;Youlong Wu;Hang Li;Shiyin Li;Guangming Shi;Naofal Al-Dhahir
With the ever-increasing user density and quality of service (QoS) demand, 5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extract...Show More
Fast Universal Azimuth Signal Modeling for Maneuvering-Platform BFSAR Imaging
Xuanqi Wang;Yachao Li;Xuan Song;Baixiao Chen;Guangming Shi;Yitao Wang;Yuzhou Liu
Appropriate modeling and processing of echoes are the foundations for high-precision frequency-domain synthetic aperture radar (SAR) imaging. The complex geometry makes it challenging to accurately characterize and cope with the 2-D spatial variation of Doppler modulation in maneuvering-platform translational-variant bistatic forward-looking SAR (MTV-BFSAR), resulting in that the azimuth processin...Show More
Uncertainty Modeling of the Transmission Map for Single Image Dehazing
Bokang Wang;Qian Ning;Fangfang Wu;Xin Li;Weisheng Dong;Guangming Shi
Despite rapid progress of end-to-end optimization for single-image dehazing, a long-standing open problem is the non-homogenous haze, at the core of the differences between synthetic hazy images and real hazy images. The atmospheric scattering model (ASM) has been widely adopted to model the degradation process of haze images but based on the assumption of homogeneous haze. In realistic scenarios,...Show More
Mobility-Aware MEC Planning With a GNN-Based Graph Partitioning Framework
Jiayi Liu;Zhongyi Xu;Chen Wang;Xuefang Liu;Xuemei Xie;Guangming Shi
Mobile service continuity is essential important to ensure that user sessions and services will survive user mobility. The 5G enhances its mobility management by providing the flexibility and offering three types of Session and Service Continuity (SSC) modes to address various service continuity requirements. Multi-access edge computing (MEC) is a type of widely adopted network architecture that d...Show More
Degradation Estimation Recurrent Neural Network With Local and Non-Local Priors for Compressive Spectral Imaging
In the coded aperture snapshot spectral imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3-D hyperspectral images (HSIs) from 2-D measurements. However, some noticeable gaps exist between the imaging model used in DUNs and the real CASSI imaging process, such as the sensing error as well as photon and dark current noise, compromising the ...Show More
Graph Transformer and LSTM Attention for VNF Multi-Step Workload Prediction in SFC
The knowledge on a Service Function Chain’s (SFC’s) resource requirements is an indispensable prerequisite for proactive resource provisioning and run-time management of the SFC. However, due to the intrinsic dynamics in network environment, accurate resource requirements and workloads prediction for the Virtual Network Functions (VNFs) of a SFC, especially in a large time scale, is a non-trivial ...Show More
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