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arXiv 2024年

作者： Wu, Fuzhi Wu, Jiasong Kong, Youyong Yang, Chunfeng Yang, Guanyu Shu, Huazhong Carrault, Guy Senhadji, Lotfi Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Bangladesh Laboratoire Traitement du Signal et de l'Image Univ Rennes France Centre de Recherche en Information Biomédicale Sino-français CRIBs France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Bangladesh

Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer structures in numerous tasks, their intricate optimization complexities highlight the persistent need for refined CNN enhancements using limited resources. Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged. The MLFM efficiently preserves low-frequency information, enhancing performance in targeted computer vision tasks. Central to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various low-frequency data and forms a parallel channel to the core network. A key advantage of MLFM is its seamless compatibility with various prevalent networks, requiring no alterations to their original core structure. Testing on ImageNet demonstrated substantial accuracy improvements in multiple 2D CNNs, including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we showcase MLFM's versatility beyond traditional image classification by successfully integrating it into image-to-image translation tasks, specifically in semantic segmentation networks like FCN and U-Net. In conclusion, our work signifies a pivotal stride in the journey of optimizing the efficacy and efficiency of CNNs with limited resources. This research builds upon the existing CNN foundations and paves the way for future advancements in computer vision. Our codes are available at https://***/AlphaWuSeu/MLFM. © 2024, CC BY-NC-ND.

关键词： computer vision

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Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment

arXiv

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arXiv 2024年

作者： Zhou, Fei Gu, Tianhao Huang, Zhicong Qiu, Guoping College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU-AFS Joint Innovation Center for Artificial Intelligence Technology Shenzhen518000 China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen518060 China School of Computer Science The University of Nottingham Ningbo China Zhejiang315100 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

The visual quality of an image is confounded by a number of intertwined factors including its semantic content, distortion characteristics and appearance properties such as brightness, contrast, sharpness, and colourfulness. Distilling high level knowledge about all these quality bearing attributes is crucial for developing objective Image Quality Assessment (IQA). While existing solutions have modeled some of these aspects, a comprehensive solution that involves all these important quality related attributes has not yet been developed. In this paper, we present a new blind IQA (BIQA) model termed Self-supervision and Vision-Language supervision Image QUality Evaluator (SLIQUE) that features a joint vision-language and visual contrastive representation learning framework for acquiring high level knowledge about the images semantic contents, distortion characteristics and appearance properties for IQA. For training SLIQUE, we have developed a systematic approach to constructing a first of its kind large image database annotated with all three categories of quality relevant texts. The Text Annotated Distortion, Appearance and Content (TADAC1) database has over 1.6 million images annotated with textual descriptions of their semantic contents, distortion characteristics and appearance properties. The method for constructing TADAC and the database itself will be particularly useful for exploiting vision-language modeling for advanced IQA applications. Extensive experimental results show that SLIQUE has superior performances over state of the art, demonstrating the soundness of its design principle and the effectiveness of its implementation. The link to the TADAC dataset is https://***/documents/tadactextannotated-distortion-appearance-and-content Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Genuine full characterization of partially coherence beam

arXiv

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arXiv 2023年

作者： Lu, Xingyuan Wang, Zhuoyi Zhan, Qiwen Cai, Yangjian Zhao, Chengliang School of Physical Science and Technology Soochow University Suzhou215006 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shandong Provincial Engineering and Technical Center of Light Manipulations Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and Electronics Shandong Normal University Jinan250358 China

For partially coherent light fields with random fluctuations, the intensity distributions and statistics have been proven to be more propagation robust compared with coherent light. However, its full potential in practical applications has not been realized due to the lack of four-dimensional optical field measurement. Here, a general modal decomposition method of partially coherent light field is proposed and demonstrated. The decomposed random modes can be used to, but not limited to, reconstruct average intensity, cross spectral density and orthogonal decomposition properties of the partially coherent light fields. Due to its versatility and flexibility, this method provides a powerful tool to further reveal light field invariant or retrieve embedded information after propagation through complex media. The Gaussian-shell-model beam and partially coherent Gaussian array are used as examples to demonstrate the reconstruction and even prediction of second-order statistical characteristics. This method is expected to pave the way for applications of partially coherent light in optical imaging, optical encryption and anti-turblence optical communication. © 2023, CC BY.

关键词： Spectral density

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Large Generative Model Assisted 3D Semantic Communication

arXiv

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arXiv 2024年

作者： Jiang, Feibo Peng, Yubo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Southeast University Nanjing China The Purple Mountain Laboratories Nanjing China

Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction;2) Latent semantic redundancy;and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Video Axle Counting and Type Recognition Method Based on Improved YOLOv5S 1

引用

6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Li, Liubin Wu, Jiawei Luo, Haibo Wu, Rongteng Li, Zuoyong College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

The number of axles of the vehicle and the type of tires can reflect the information of the vehicle to a certain extent, and the load capacity can be calculated according to the number of axles of the truck and the type of axles. Therefore, the identification of the axle is of great significance for judging whether the truck is overweight. At present, the method of calculating the axles is carried out by the method of Laser Radar or grating for axle counting. In the prior art, the method of axle counting is complicated to deploy and the cost is high. Some computer vision-based axle statistics methods have emerged in recent years, but complete vehicle sideways pictures are required. However, due to the long body of the truck and the limited space factor, it is difficult to obtain the complete vehicle in an original image. Although image stitching can solve this problem, the current video image stitching methods have a relatively high time cost. To solve this issue, we propose an object detection and tracking method based on YOLOv5s for axle counting and tire type identification. Experimental results show that the proposed method has extremely high accuracy and can meet real-time requirements even without GPU. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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FAST: a dual-tier few-shot learning paradigm for whole slide image classification 24

FAST: a dual-tier few-shot learning paradigm for whole slide...

引用

Proceedings of the 38th International Conference on Neural information processing Systems

作者： Kexue Fu Xiaoyuan Luo Linhao Qu Shuo Wang Ying Xiong Ilias Maglogiannis Longxiang Gao Manning Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Fudan University University of Piraeus

ISBN: (纸本)9798331314385

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST.

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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Shujiang Xu Fansheng Wang Lianhai Wang Miodrag J. Mihaljević Shuhui Zhang Wei Shao Qihao Huang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Mathematical Institute the Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users’ identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible.

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Text information Mining in Cyberspace: An information Extraction Method Based on T5 and keyBERT

Text Information Mining in Cyberspace: An Information Extrac...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Kuan Liu Yumei Li Yuanhua Qi Ning Qi Mengran Zhai Information Research Institute of Shandong Academy of Sciences Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Institute of Economy and Informatization Development Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and keyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value.

关键词： Measurement Biological system modeling Cyberspace Text summarization information processing information retrieval Transformers Data models Data mining information entropy

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A Novel Multi-Agent Deep Reinforcement Learning Approach

A Novel Multi-Agent Deep Reinforcement Learning Approach

引用

2020 International Conference on computer Big Data and Artificial Intelligence, ICCBDAI 2020

作者： Yin, Dong Zhao, Zhe Dai, Yinglong Long, Han College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still restricted to single agent training environment. Multi-agent reinforcement learning still is a challenge problem. Although some multi-agent deep reinforcement learning methods have been proposed, they can only perform well when the number of agents is very limited. In this paper, by analyzing the dynamic changing observation space and action space of multi-agent environment, we propose a novel multi-agent deep RL method that compress the joint observation space and action space as the time goes on. The proposed method is potential for a large number of agents cooperative or competitive tasks. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions

MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions

引用

6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： Zili Chen Yilong Peng Yaokai Yang Jiadong Jiao Laigang Wang Aiguo Wang Jianjun Liu Wei Lin Yan Guo College of Computer and Information Engineering Henan Normal University Xinxiang China School of Tourism Henan Normal University Xinxiang China Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences Zhengzhou China Zhengzhou Tobacco Research Institute of CNTC Zhengzhou China Henan Provincial Tobacco Company of CNTC Zhengzhou China Henan Provincial Key Laboratory of Educational Artificial Intelligence and Personalized Learning Xinxiang China Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology Ministry of Agriculture and Rural Affairs Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

For tobacco leaf disease spots with varying scales and shapes causing difficulties in segmentation and low accuracy, a novel MD_Unet algorithm is proposed for precise segmentation of tobacco leaf disease spots, aiding in scientific prevention and control of tobacco diseases. Innovative design includes Multi-scale Convolution Module (MC) and Dense Residual Dilated Convolution Module (MVCR) to enhance feature extraction of multiscale disease spots and perception of global contextual information. Utilizing Residual Unit Fusion Framework (RUFF) in the upsampling section effectively compensates for spatial details loss and strengthens network representation capability. Additionally, integration of Channel Spatial Attention Mechanism (CBAM) and Attention Gate (AG) further enhances the network's focusing ability on key information and fine-grained feature extraction capability. The MD_Unet algorithm achieves significant improvements in spot CPA, Recall, IoU, F1 metrics with 92.75%, 90.94 % ,84.93 % ,91.81% respectively, and an overall Dice score of 94.67%. Compared to mainstream methods like Unet, PSP, DeepLab v3+, FCN, SegNet, UNET++, DoubleU-Net, it shows remarkable enhancements, providing theoretical basis and technical support for precise segmentation of tobacco leaf disease spots and other plant diseases.

关键词： Location awareness Accuracy Shape Convolution Signal processing algorithms Logic gates Feature extraction Robustness Lesions Diseases

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