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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

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Research and application based on simulation of ESM model 3

Research and application based on simulation of ESM model

引用

3rd international conference on Advanced Algorithms and Signal image processing, AASIP 2023

作者： Wang, Jingyi Fu, Yucheng Zhang, Yang Computer Science Shanghai University Shanghai China Mathematics Shanghai University Shanghai China

ISBN: (纸本)9781510668522

In this paper, simulation is based on the ESM model, which is traditionally characterized by high nonlinearity and high uncertainty. The complexity of the system poses a great challenge for its overall characterization. Modern simulation technology can perform detailed numerical modeling and simulation calculations to realize the analysis and prediction of the system operation state, providing an effective research tool and decision support for the construction of the ESM model optimization system. In this paper, the simulation application of ESM model is based on matrix equation and dynamical system, and the sensitivity analysis is attempted, which proves that the simulation effect of ESM model is good and has good research value and application significance, in order to provide some reference significance for the research in other fields. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Sensitivity analysis

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Research on Test Parameters of Ultra High Resolution CMOS image Sensor Microsystems 2

Research on Test Parameters of Ultra High Resolution CMOS Im...

引用

2nd international conference on Automation, Robotics and computer Engineering, ICARCE 2023

作者： Tian, Yu Liu, Pei Rui, Erming Jiao, Qiang Xiong, Yuanyuan Dong, Yanlei Ma, Yue Li, Junlin China Astronautics Standards Institute Space Components Guarantee Research Institute Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China

ISBN: (纸本)9798350308341

With the development of the information society and the rapid rise of new generation information technology industries represented by smart earth, the internet of things, and cloud computing, the data transmission and processing capabilities has faced unprecedented challenges for the existing visual information perception methods, which limits the improvement of imaging system characteristics. The ultra high resolution CMOS image sensor microsystem is a groundbreaking product which leads the future development of the optoelectronic imaging field. This manuscript mainly focuses on the research ideas of Test Parameters for ultra high resolution CMOS image sensor microsystem. Moreover, the optoelectronic/spectral parameter testing and image quality parameter testing are similar with the traditional CMOS image sensor, and the new Test Parameters derived mainly include geometric parameter testing and complex scene multi object recognition testing according to the characteristics of the ultra high resolution CMOS image sensing microsystem. In addition, this manuscript proposes aspects that need to be improved based on new products in terms of testing methods for optoelectronic/spectral parameters and image quality parameters. A preliminary set of Test Parameters for new products has been established. Adopting targeted testing methods is an important part of subsequent performance evaluation of new products. © 2023 IEEE.

关键词： CMOS integrated circuits

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Implementing Machine Learning Algorithms to Indentify Distributed Denial-of-Service Attacks 7

Implementing Machine Learning Algorithms to Indentify Distri...

引用

7th international conference on image Information processing, ICIIP 2023

作者： Mehra, Ankush Singh, Gurpreet Badotra, Sumit Kaur, Balwinder Verma, Amit Lovely Professional University Department of Computer Science And Engineering Punjab Phagwara India Bennett University School of Computer Science Engineering And Technology Greater Noida India University Center For Research And Development Chandigarh University Department of Computer Science And Engineering Mohali Gharuan India

ISBN: (纸本)9798350371406

An Internet server, service, or network may be the subject of a distributed denial-of-service (DDoS) assault, in which the attacker attempts to interrupt regular traffic by flooding the target with an excessive amount of data requests. Network security has been severely compromised as a result. We choose the vast majority of network parameters to characterize the traffic in order to get a complete picture of it. As a further step toward lowering the detection time complexity, we employ the PCA approach to compress the features' dimensions. Using principal component analysis (PCA), we can save much of the original data while drastically cutting down on forecast time. After data dimensionality reduction, the RNN is used to train and get a detection model. In comparison to other approaches for detecting DDoS assaults, PCA-RNN performs much better on the real-world dataset, as shown by the evaluation results. © 2023 IEEE.

关键词： Principal component analysis

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image processing based Skin Cancer Recognition using Machine Learning 3

Image Processing based Skin Cancer Recognition using Machine...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Singh, Sanjay Pilania, Urmila Kumar, Manoj Awasthi, Satya Prakash Manav Rachna University Dept. of Computer Science & Technology Faridabad India Poornima University Faculty of Computer Engineering Jaipur India

ISBN: (纸本)9798350338607

The skin is the most important part of the human body because it protects bones and muscles., hence, it is the body's largest organ. Numerous people are suffering from skin cancer these days. It has now become the most common type of cancer. Melanoma is known to be a serious and mortal disease. If detected in initial stages, it can be easily treated. Non-melanoma skin cancers have also become common. Rrecognizing skin cancer automatically from the images can help in faster diagnosis and enhanced accuracy saving valuable time. In this paper, 4 types of CNN were used named VGG16, InceptionV3, ResNet152V2 and a special CNN with 12 layers. © 2023 IEEE.

关键词： Machine learning

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MDVT: A Multi-modal Fake News Detection Framework based on vision Transformer 6

MDVT: A Multi-modal Fake News Detection Framework based on V...

引用

6th international conference on Machine Learning and Natural Language processing, MLNLP 2023

作者： Wu, Yuexin Tang, Yi Fan, Chunxiao Liu, Yu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Beijing-Dublin International College Beijing University of Technology Beijing China

ISBN: (纸本)9798400709241

The growth of social media in recent years has contributed to the spread of fake news on the Internet. Since multimodal contents such as pictures have a huge impact on the spread of news in social media, researchers are increasingly focusing on the automatic detection of multimodal false news. Existing multimodal approaches disregard the recent development results of vision pretraining, consequently, their visual feature extractor primarily uses early proposed convolutional neural networks, such as VGG and ResNet. Compared with vision Transformers, traditional convolution neural networks have insufficient ability to extract local semantics from images as well as capture the relationships between different regions in images, which may reduce the performance of multimodal methods. To address this issue, we propose a multimodal fake news detection framework based on vision Transformer(MDVT). By experimenting with different multi-modal combination methods and feature extractors on this framework, we get a top-performing which combined MacBERT text features and Swin Transformer image features through the maximum method, named MDVT-MS. Experimental studies on a widely used social media dataset Weibo demonstrate that MDVT-MS outperforms current state-of-The-Art multimodal detection methods by 4.4% in accuracy. This proves the effectiveness of the proposed method and shows that vision Transformer can be used as a new vision backbone for multimodal fake news detection. © 2023 ACM.

关键词： Modal analysis

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Swin-UNIT: Transformer-based GAN for High-resolution Unpaired image Translation 23

Swin-UNIT: Transformer-based GAN for High-resolution Unpaire...

引用

31st ACM international conference on Multimedia (MM)

作者： Li, Yifan Li, Yaochen Tang, Wenneng Zhu, Zhifeng Yang, Jinhuo Liu, Yuehu Xi An Jiao Tong Univ Sch Software Engn Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Inst AI & Robot Xian Shaanxi Peoples R China

ISBN: (纸本)9798400701085

The transformer model has gained a lot of success in various computer vision tasks owing to its capacity of modeling long-range dependencies. However, its application has been limited in the area of high-resolution unpaired image translation using GANs due to the quadratic complexity with the spatial resolution of input features. In this paper, we propose a novel transformer-based GAN for high-resolution unpaired image translation named Swin-UNIT. A two-stage generator is designed which consists of a global style translation (GST) module and a recurrent detail supplement (RDS) module. The GST module focuses on translating low-resolution global features using the ability of self-attention. The RDS module offers quick information propagation from the global features to the detail features at a high resolution using cross-attention. Moreover, we customize a dual-branch discriminator to guide the generator. Extensive experiments demonstrate that our model achieves state-of-the-art results on the unpaired image translation tasks.

关键词： transformer high-resolution unpaired image translation

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CROSS-LAYER RETROSPECTIVE RETRIEVING VIA LAYER ATTENTION 11

CROSS-LAYER RETROSPECTIVE RETRIEVING VIA LAYER ATTENTION

引用

11th international conference on Learning Representations, ICLR 2023

作者： Fang, Yanwen Cai, Yuxi Chen, Jintai Zhao, Jingyu Tian, Guangjian Li, Guodong Department of Statistics & Actuarial Science The University of Hong Kong Hong Kong College of Computer Science and Technology Zhejiang University China Huawei Noah's Ark Lab Canada

More and more evidence has shown that strengthening layer interactions can enhance the representation power of a deep neural network, while self-attention excels at learning interdependencies by retrieving query-activated *** by this, we devise a cross-layer attention mechanism, called multi-head recurrent layer attention (MRLA), that sends a query representation of the current layer to all previous layers to retrieve query-related information from different levels of receptive fields.A light-weighted version of MRLA is also proposed to reduce the quadratic computation *** proposed layer attention mechanism can enrich the representation power of many state-of-the-art vision networks, including CNNs and vision *** effectiveness has been extensively evaluated in image classification, object detection and instance segmentation tasks, where improvements can be consistently *** example, our MRLA can improve 1.6% Top-1 accuracy on ResNet-50, while only introducing 0.16M parameters and 0.07B ***, it can boost the performances by a large margin of 3-4% box AP and mask AP in dense prediction *** code is available at https://***/joyfang1106/MRLA. © 2023 11th international conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Object detection

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A Novel Framework for Potato Leaf Disease Detection Using Deep Learning Model 14

A Novel Framework for Potato Leaf Disease Detection Using De...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Paul, Aniket Jain, Bhaktram Saranya, G. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350335095

computer vision has recently developed into a crucial tool in agriculture for tracking plant health and treating illnesses. In this paper, we present a VGG19-based deep learning technique for recognising diseases in Potato leaves. Bacterial spot, early blight, late blight, and spider mite damage are four prevalent potato leaf diseases that the suggested system can detect automatically. 1,000 photos per class make up the 4,000 images of Potato leaves in the dataset used for this study. The photos in the dataset were resized and divided into training, validation, and testing sets as part of the pre-processing step. On the Potato leaf dataset, we refined the pre-trained VGG19 model using the transfer learning technique. According to experimental data, our suggested strategy outperformed other cutting-edge techniques, achieving an overall accuracy of 95.2% on the test set. Additionally, we compared various deep learning architectures and discovered that VGG19 is the best architecture for this task. A significant tool for farmers and agronomists, the suggested Potato leaf disease detection system might help them identify illnesses early and take the necessary precautions to stop their spread. © 2023 IEEE.

关键词： computer vision

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Convolutional Autoencoder for vision-Based Human Activity Recognition 15th

Convolutional Autoencoder for Vision-Based Human Activity Re...

引用

15th international conference on Intelligent Human-computer Interaction (IHCI)

作者： Jain, Surbhi Garg, Aishvarya Nigam, Swati Singh, Rajiv Shastri, Anshuman Singh, Irish Banasthali Vidyapith Dept Comp Sci Jaipur 304022 Rajasthan India Banasthali Vidyapith Dept Phys Sci Jaipur 304022 Rajasthan India Banasthali Vidyapith Ctr Artificial Intelligence Jaipur 304022 Rajasthan India Oregon Inst Technol Klamath Falls OR USA

ISBN: (纸本)9783031538292;9783031538308

Human activity recognition (HAR) is a crucial component for many current applications, including those in the healthcare, security, and entertainment sectors. At the current state of the art, deep learning outperformsmachine learning with its ability to automatically extract features. Autoencoders (AE) and convolutional neural networks (CNN) are the types of neural networks that are known for their good performance in dimensionality reduction and image classification, respectively. As most of the methods introduced for classification purposes are limited to sensor based methods. This paper mainly focuses on vision based HAR where we present a combination of AE and CNN for the classification of labeled data, in which convolutional AE (conv-AE) is utilized for two functions: dimensionality reduction and feature extraction and CNN is employed for classifying the activities. For the proposed model's implementation, public benchmark datasets KTH andWeizmann are considered, on which we have attained a recognition rate of 96.3%, 94.89% for both, respectively. Comparative analysis is provided for the proposed model for the above-mentioned datasets.

关键词： Human Activity Recognition Deep Learning Convolutional Neural Networks Autoencoders Convolutional Autoencoders

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Scaling vision-based End-to-End Autonomous Driving with Multi-View Attention Learning

Scaling Vision-based End-to-End Autonomous Driving with Mult...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Xiao, Yi Codevilla, Felipe Porres, Diego Lopez, Antonio M. Univ Autonoma Barcelona UAB Dept Comp Sci Comp Vis Ctr CVC Barcelona Spain Montreal Inst Learning Algorithms MILA Montreal PQ Canada

ISBN: (纸本)9781665491907

On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning.

关键词： image enhancement

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