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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

IEEE Transactions on Image Processing 2025年 34卷 3139-3153页

作者： Chen, Jun Yang, Liling Yu, Wei Gong, Wenping Cai, Zhanchuan Ma, Jiayi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China China University of Geosciences Faculty of Engineering Wuhan430074 China Macau University of Science and Technology School of Computer Science and Engineering Taipa China Wuhan University Electronic Information School Wuhan430072 China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. © 1992-2012 IEEE.

关键词： Image coding

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Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices

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National science Review 2022年第11期9卷 172-181页

作者： Shufang Zhao Wenhao Ran Zheng Lou Linlin Li Swapnadeep Poddar Lili Wang Zhiyong Fan Guozhen Shen State Key Laboratory for Superlattices and Microstructures Institute of SemiconductorsChinese Academy of Sciences and Center of Materials Science and Optoelectronic EngineeringUniversity of Chinese Academy of Sciences Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology School of Integrated Circuits and Electronics Beijing Institute of Technology

High-accuracy neuromorphic devices with adaptive weight adjustment are crucial for high-performance computing. However, limited studies have been conducted on achieving selective and linear synaptic weight updates without changing electrical pulses. Herein, we propose high-accuracy and self-adaptive artificial synapses based on tunable and flexible MXene energy storage devices. These synapses can be adjusted adaptively depending on the stored weight value to mitigate time and energy loss resulting from recalculation. The resistance can be used to effectively regulate the accumulation and dissipation of ions in single devices, without changing the external pulse stimulation or preprogramming, to ensure selective and linear synaptic weight updates. The feasibility of the proposed neural network based on the synapses of flexible energy devices was investigated through training and machine learning. The results indicated that the device achieved a recognition accuracy of ～95% for various neural network calculation tasks such as numeric classification.

关键词： neuromorphic computing MXene flexible device high accuracy adaptability ion dynamics

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A Novel Unsupervised MRI Synthetic CT Image Generation Framework with Registration Network

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computers, Materials & Continua 2023年第11期77卷 2271-2287页

作者： Liwei Deng Henan Sun Jing Wang Sijuan Huang Xin Yang Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration School of AutomationHarbin University of Science and TechnologyHarbin150080China Institute for Brain Research and Rehabilitation South China Normal UniversityGuangzhou510631China Department of Radiation Oncology Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapyGuangzhou510060China

In recent years,radiotherapy based only on Magnetic Resonance(MR)images has become a hot spot for radiotherapy planning research in the current medical ***,functional computed tomography(CT)is still needed for dose calculation in the *** deep-learning approaches to synthesized CT images from MR images have raised much research interest,making radiotherapy based only on MR images *** this paper,we proposed a novel unsupervised image synthesis framework with registration *** paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image and registering the cycle-consistent image with the input ***,this paper added ConvNeXt blocks to the network and used large kernel convolutional layers to improve the network’s ability to extract *** research used the collected head and neck data of 180 patients with nasopharyngeal carcinoma to experiment and evaluate the training model with four evaluation *** the same time,this research made a quantitative comparison of several commonly used model *** evaluate the model performance in four evaluation metrics which achieve Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity(SSIM)are 18.55±1.44,86.91±4.31,33.45±0.74 and 0.960±0.005,*** with other methods,MAE decreased by 2.17,RMSE decreased by 7.82,PSNR increased by 0.76,and SSIM increased by *** results show that the model proposed in this paper outperforms other methods in the quality of image *** work in this paper is of guiding significance to the study of MR-only radiotherapy planning.

关键词： MRI-CT image synthesis variational auto-encoder medical image translation MRI-only based radiotherapy

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Steel Surface Automatic Detection and Segmentation Based on an Improved Faster R-CNN 43

Steel Surface Automatic Detection and Segmentation Based on ...

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43rd Chinese Control Conference, CCC 2024

作者： Zhu, Qingyuan Wei, Tao Xu, Shanbo Gao, Yunlong Shao, Guifang Li, Zhichao Xiamen University Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen China State Key Laboratory of Intelligent Agricultural Power Equipment Henan China Xiamen University Department of Automation Xiamen China

ISBN: (纸本)9789887581581

Despite the application of various deep learning networks and machine learning algorithms for steel surface defect detection, achieving satisfactory accuracy remains a challenge. To address issues such as dynamic image quality, insignificant features, and varied defect sizes, this paper proposes an enhanced Faster R-CNN method termed IFRC. To mitigate the impact of uneven illumination, normalization techniques and vertical mean removal are employed. Moreover, to augment the significance of defect features, dense connection is integrated into the VGG-16 architecture. Furthermore,an anchor parameter adjustment method based on K-means clustering is introduced to detect long, thin defects effectively. To enhance the segmentation of patches by eliminating holes and edge burrs, the Felzenswalb algorithm is utilized to refine the results. Experimental results conducted on the NEU-DET dataset demonstrate the superior performance of IFRC compared to Faster R-CNN. Moreover, IFRC achieves better segmentation results than DLRSD. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： K-means clustering

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NfvInsight:A Framework for Automatically Deploying and Benchmarking VNF Chains

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Journal of computer science & technology 2022年第3期37卷 680-698页

作者： Tian-Ni Xu Hai-Feng Sun Di Zhang Xiao-Ming Zhou Xiu-Feng Sui Sa Wang Qun Huang Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Peng Cheng Laboratory Shenzhen 518055China Department of Computer Science and Technology Peking UniversityBeijing 100871China

With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exists a performance *** to narrow the gap is an essential issue of current NFV ***,the cumbersomeness of deployment,the water pipe effect of virtual network function(VNF)chains,and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV *** pinpoint the NFV system performance,we propose NfvInsight,a framework for automatic deployment and benchmarking VNF *** framework tackles the challenges in NFV performance *** framework components include chain graph generation,automatic deployment,and fine granularity *** design and implementation of each component have their *** the best of our knowledge,we make the first attempt to collect rules forming a knowledge base for generating reasonable chain *** deploys the generated chain graphs automatically,which frees the network operators from executing at least 391 lines of bash commands for a single *** diagnose the performance bottleneck,NfvInsight collects metrics from multiple layers of the software ***,we collect the network stack latency distribution ingeniously,introducing only less than 2.2%*** showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying *** our framework,we find several design flaws of the network stack,which are unsuitable for packet forwarding inside one single server under the NFV *** optimization for these flaws gains at most 3x performance improvement.

关键词： network function virtualization(NFV) service chain performance bottleneck network stack latency

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Near-Space Communications: The Last Piece of 6G Space–Air–Ground–Sea Integrated Network Puzzle

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Space(science & technology) 2024年第1期4卷 183-203页

作者： Hongshan Liu Tong Qin Zhen Gao Tianqi Mao Keke Ying Ziwei Wan Li Qiao Rui Na Zhongxiang Li Chun Hu Yikun Mei Tuan Li Guanghui Wen Lei Chen Zhonghuai Wu Ruiqi Liu Gaojie Chen Shuo Wang Dezhi Zheng School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of CNS/ATM Beijing Institute of TechnologyBeijing 100081China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology(Zhuhai) Zhuhai 519088China Advanced Technology Research Institute Beijing Institute of TechnologyJinan 250307China Yangtze Delta Region Academy Beijing Institute of Technology(Jiaxing)Jiaxing 314019China Department of Systems Science Southeast UniversityNanjing 210096China Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology School of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China Wireless and Computing Research Institute ZTE CorporationBeijing 100029China G/6G Innovation Centre Institute for Communication SystemsUniversity of SurreyGU27XH GuildfordUK China Academy of Space Technology Beijing 100094China

This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space–air–ground–sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics, and unique problems of NS-COM network. Afterward, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management, and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring, and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite-to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output and holographic multiple-input multiple-output, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, stratospheric-satellite-based radar detection and imaging, NS-COM-assisted enhanced global navigation system, NS-COM-assisted intelligent unmanned system, and free-space optical communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture.

关键词： comparative analysis random access near space communications channel modeling joint network optimization array based beam management space air ground sea integrated network channel estimation

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Unleashing the Power of Large Language Model for Denoising Recommendation 25

Unleashing the Power of Large Language Model for Denoising R...

引用

34th ACM Web Conference, WWW 2025

作者： Wang, Shuyao Zheng, Zhi Sui, Yongduo Xiong, Hui School of Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei China Tencent Shenzhen China Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400712746

Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve incorporating auxiliary information or learning strategies from interaction data. However, they struggle with the inherent limitations of external knowledge and interaction data, as well as the non-universality of certain predefined assumptions, hindering accurate noise identification. Recently, large language models (LLMs) have gained attention for their extensive world knowledge and reasoning abilities, yet their potential in enhancing denoising in recommendations remains underexplored. In this paper, we introduce LLaRD, a framework leveraging LLMs to improve denoising in recommender systems, thereby boosting overall recommendation performance. Specifically, LLaRD generates denoising-related knowledge by first enriching semantic insights from observational data via LLMs and inferring user-item preference knowledge. It then employs a novel Chain-of-Thought (CoT) technique over user-item interaction graphs to reveal relation knowledge for denoising. Finally, it applies the Information Bottleneck (IB) principle to align LLM-generated denoising knowledge with recommendation targets, filtering out noise and irrelevant LLM knowledge. Empirical results demonstrate LLaRD’s effectiveness in enhancing denoising and recommendation accuracy. © 2025 Copyright held by the owner/author(s).

关键词： Recommender systems

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Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation

Exploring Regional Clues in CLIP for Zero-Shot Semantic Segm...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yi Zhang Meng-Hao Guo Miao Wang Shi-Min Hu State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Department of Computer Science and Technology BNRist Tsinghua University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

CLIP has demonstrated marked progress in visual recognition due to its powerful pre-training on large-scale image-text pairs. However, it still remains a critical challenge: how to transfer image-level knowledge into pixel-level understanding tasks such as semantic segmentation. In this paper, to solve the mentioned challenge, we analyze the gap between the capability of the CLIP model and the requirement of the zero-shot semantic segmentation task. Based on our analysis and observations, we propose a novel method for zero-shot semantic segmentation, dubbed CLIP-RC (CLIP with Regional Clues), bringing two main insights. On the one hand, a region-level bridge is necessary to provide fine-grained semantics. On the other hand, over-fitting should be mitigated during the training stage. Benefiting from the above discoveries, CLIP-RC achieves state-of-the-art performance on various zero-shot semantic segmentation benchmarks, including PASCAL VOC, PASCAL Context, and COCO-Stuff 164K. Code will be available at https://***/Jittor/JSeg.

关键词： Training Bridges Visualization computer vision Image recognition Codes Semantic segmentation

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

引用

9th International Conference on intelligent Computing and Signal Processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities:A Survey

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Machine Intelligence Research 2022年第3期19卷 184-208页

作者： Xiao-Qing Zhang Yan Hu Zun-Jie Xiao Jian-Sheng Fang Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Tomey Corporation Nagoya 4510051Japan Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315300China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and TechnologyShenzhen 518055China

Cataracts are the leading cause of visual impairment and blindness *** the years,researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading,aiming to prevent cataracts early and improve clinicians′diagnosis *** survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic *** summarize existing literature from two research directions:conventional machine learning methods and deep learning *** survey also provides insights into existing works of both merits and *** addition,we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.

关键词： Cataract classification and grading ophthalmic image machine learning deep learning

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