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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Faster Meta Update Strategy for Noise-Robust Deep Learning

Faster Meta Update Strategy for Noise-Robust Deep Learning

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xu, Youjiang Zhu, Linchao Jiang, Lu Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta-learning employs a meta model for correcting the training bias. Despite the promising performances, super slow training is currently the bottleneck in the meta learning approaches. In this paper, we introduce a novel Faster Meta Update Strategy (FaMUS) to replace the most expensive step in the meta gradient computation with a faster layer-wise approximation. We empirically find that FaMUS yields not only a reasonably accurate but also a low-variance approximation of the meta gradient. We conduct extensive experiments to verify the proposed method on two tasks. We show our method is able to save two-thirds of the training time while still maintaining the comparable or achieving even better generalization performance. In particular, our method achieves the state-of-the-art performance on both synthetic and realistic noisy labels, and obtains promising performance on long-tailed recognition on standard benchmarks.

关键词： Training Deep learning computer vision Training data Robustness pattern recognition Noise robustness

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AdaBoost-SVM Face recognition Algorithm Based on LBP and Gabor Feature Fusion 5

AdaBoost-SVM Face Recognition Algorithm Based on LBP and Gab...

引用

5th International conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Lian, Xiaoqin Liu, Chunquan Gao, Chao Ma, Guochun Gong, Yonggang Li, Jin Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing100048 China China National Light Industry Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data Beijing100048 China

ISBN: (纸本)9798331506612

In recent years, facial recognition technology has seen rapid advancements and is now extensively utilized in security surveillance and financial transactions. The face recognition process usually includes image preprocessing, feature extraction, and classification. However, it is often difficult for a single feature to fully capture the complex features of a human face, thus affecting the accuracy of recognition. To address this issue, this study presented an AdaBoost-SVM face recognition algorithm that fuses local binary pattern and Gabor features, which can enable comprehensive extraction of facial features from images and enhance recognition accuracy. This method first performs bilateral filtering on the face image to reduce noise and retain edge information. Then, it extracts the local binary pattern and Gabor features and weights the fusion. Finally, it uses the AdaBoost-SVM classifier for identification. Moreover, the algorithm performance was comprehensively evaluated using the ten-fold cross-validation method. The experimental results demonstrated that this method achieves recognition accuracy of 98.75% and 96.25% on ORL and Yale A face databases respectively, and still performs well in complex lighting and expression-changing environments, significantly improving the accuracy of face recognition and its robustness. © 2024 ieee.

关键词： Local binary pattern

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Research on Water Conservancy Project Monitoring and Intelligent Identification Method Based on computer vision Technology

Research on Water Conservancy Project Monitoring and Intelli...

引用

2024 International conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Zhu, Jin Yang, Feng Zhejiang Province Water Conservancy and Hydropower Technology Consultation Center Zhejiang Hangzhou310009 China Zhejiang Zhongshui Engineering Technology Co. Ltd. Hangzhou310020 China

ISBN: (纸本)9798350369212

Monitoring and maintenance of water resources projects is essential to ensure project safety and environmental protection. Traditional monitoring methods often rely on manual inspections and sensor data, but these methods suffer from high cost, low efficiency and limited monitoring range. With the rapid development of computer vision technology, its potential for application in water conservancy engineering has gradually emerged. The purpose of this paper is to study the water conservancy project monitoring and intelligent identification methods based on computer vision technology. We first outline the basic concepts of computer vision and its application scenarios in water conservancy engineering, including structural health monitoring, environmental monitoring and disaster management. Then, the specific applications of image processing techniques, machine learning and deep learning in intelligent recognition are described in detail, especially how these techniques can be utilized to achieve an efficient and accurate real-time monitoring system. Through several case studies, this paper demonstrates the effects and advantages of the application of computer vision technology in actual water conservancy projects. Finally, it summarizes the main findings of the current study and looks forward to the future technological development and application prospects, pointing out the direction of further research. The findings of this paper show that computer vision-based monitoring and intelligent identification methods not only improve the accuracy and efficiency of monitoring, but also provide new technical means for the management and maintenance of water conservancy projects. © 2024 ieee.

关键词： Environmental monitoring

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Yolo algorithms to improve the detection of personal protective equipment thought industrial computer vision 30

Yolo algorithms to improve the detection of personal protect...

引用

30th ieee International conference on Electronics, Electrical Engineering and Computing, INTERCON 2023

作者： Velasquez, Ricardo Arias Lara, Jennifer Vanessa Mejia Velasquez, Renato Arias Universidad Tecnológica Del Perú Lima Peru Universidad Nacional de San Agustín de Arequipa Lima Peru

ISBN: (纸本)9798350315578

During 2023, accidents at work are unexpected situations that cause losses in both human and material resources. In Peru there is current legislation that encourages a safe work environment, however, according to 2019 reports from the Ministry of Labor and Employment Promotion, 31.92% of accidents caused this year directly affected the cranial area. The use of personal protective equipment requires finding an optical way to identify the required safety equipment in real time. In response to this problem, the use of artificial vision or computer vision for helmet recognition. The trained model for online personal protective equipment classifier algorithms with computer vision in industrial applications allows to detect helmets, head, safety glasses, industrial vests with precision metrics greater than 88% according to light intensity. Additionally, it is possible to improve the accuracy of the other clusters by training with higher epochs, increasing batch processing from eight to sixteen, and using parallel processing. Despite the fact that the configuration profile for the training process carried out was basic, the database used, due to the varied labeling and number of images, acceptable results were obtained. Also, due to the selection of the YOLOV5m model, transfer learning training contributed to the training time without reducing the accuracy rates. Our findings is the recognition of a model with 5000 pictures with an accuracy of 88% from 400 to 800 lux and limited for the light intensity lower than 400 lux, with 78.63%. With light intensity higher than 800 lux, the recognition time of less than 0.3 seconds/image together with a rate of 98% with a low-cost camera and parallel processing with a Raspberry Pi version 4. © 2023 ieee.

关键词： computer vision

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Drowsy Driver Detection System using computer vision and Transfer Learning for Preventing Road Accidents 3

Drowsy Driver Detection System using Computer Vision and Tra...

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3rd ieee International conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Bagal, Naisargi Dingreja, Kashish Atterkar, Atharva Bachhav, Prasad Sable, Nilesh P. Yenkikar, Anuradha Alkunte, Ritesh Vishwakarma Institute of Information Technology Department of Computer Science and Engineering - Artificial Intelligence Pune India

ISBN: (纸本)9798350354348

Major traffic accidents are attributed to driver fatigue, according to study on the topic. Driver drowsiness is a state in which the driver of a car is on the verge of falling asleep or losing consciousness. It can be brought on by a number of variables, including biological, physical, psychological, and other factors, all of which can impede and inhibit safe driving. The several methods that can be applied to the implementation of a system for the detection of driver sleepiness will be reviewed in this paper. Improving the ability to identify drowsiness in real time through computer vision is the main objective of all technologies. Thus, our work also pertains to the identification of driver drowsiness, whereby a motorist's tiredness can be ascertained by first recognizing their face, followed by eye tracking. The system compares the extracted eye image with the dataset. The system used the dataset and predicts using transfer learning with our base model as InceptonV3 to identify that it could alert the driver with an alarm if the driver's eyes were closed for a predetermined amount of time, and it could resume monitoring if the driver's eyes were open following the alarm alert. We established a score that grew if the eyes were closed and dropped if they were open. This study aims to reduce traffic accidents by solving the sleepiness detection problem with InceptionV3 with an accuracy of 95.46% compare to 92.11% of ***, driving drowsiness detection - which assesses a driver's degree of fatigue using facial recognition and eye tracking becomes the main focus of our research. © 2024 ieee.

关键词： Face recognition

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Shot in the Dark: Few-Shot Learning with No Base-Class Labels

Shot in the Dark: Few-Shot Learning with No Base-Class Label...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Zitian Maji, Subhransu Learned-Miller, Erik Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665448994

Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base classes'. The difference in data distribution between the test set (novel classes) and the base classes used to learn an inductive bias often results in poor generalization on the novel classes. To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting. In this work, we show that, surprisingly, off-the-shelf self-supervised learning outperforms transductive few-shot methods by 3.9% for 5-shot accuracy on miniImageNet without using any base class labels. This motivates us to examine more carefully the role of features learned through self-supervision in few-shot learning. Comprehensive experiments are conducted to compare the transferability, robustness, efficiency, and the complementarity of supervised and self-supervised features.

关键词： computer vision conferences Supervised learning Robustness pattern recognition

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Deep Learning-Powered Face recognition Attendance System: A Novel Approach to Automated Prediction 2

Deep Learning-Powered Face Recognition Attendance System: A ...

引用

2nd International conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sornalakshmi, M. Abinash, M.J. Gopalakrishnan, S. Kasthuri, R. Nivedhitha Harini, C.M. Balamurugan, K. Department of Computer Science PG Arulmigu Kalasalingam College of Arts and Science Tamilnadu India Arulmigu Kalasalingam College of Arts and Science Tamilnadu India

ISBN: (纸本)9798350340235

Taking attendance is one of the most critical activities that must be completed on a regular basis at colleges, universities, organizations, schools, and companies. The bulk of the time, it is done manually, such as by name or roll number. The primary purpose of this work is to develop a Face recognition-based attendance system that will automate this human procedure. This research work fits the standards for modernizing the way attendance is handled, as well as the time management criteria. This gadget is put in the classroom and is programmed with student information such as name, roll number, class, sec, and photos. Open computer vision (CV) is used to extract the photos. The understudy can move toward the machine before the beginning of the connected example, and it will start taking photos and contrasting them with the passing dataset. The camera and processor board in this task were a Logitech C270 web camera and a NVIDIA Jetson Nano Engineer pack. The image is changed as follows: Initial, a Haar Cascade classifier is utilized to recognize faces, trailed by the LBPH (Neighborhood Twofold Example Histogram) Calculation, histogram information is considered in contrast to a preset dataset, and the gadget consequently distinguishes participation. A Succeed document is made and refreshed consistently with data from the pertinent class educator. The accompanying work summarizes the fundamental elements of a facial recognition -based attendance system. © 2023 ieee.

关键词： Face recognition Local Binary pattern Histogram Open computer vision

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Micro-expression recognition based on Spatiotemporal Local Gabor Binary patterns on Three Orthogonal Planes

Micro-expression Recognition based on Spatiotemporal Local G...

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2023 International conference on Image, Signal Processing, and pattern recognition, ISPP 2023

作者： Fan, Ling Lin, Chenhan Computer Science and Information Engineering School Xiamen Institute of Technology Xiamen361021 China Center for Digital Media Computation of Xiamen University Xiamen361005 China

ISBN: (纸本)9781510666351

Analyzing facial micro-expressions based on computer vision has become an active research area in recent years. Subtle changes and short duration of micro-expressions bring great challenges to this issue, and extracting effective spatiotemporal features is the key to improving recognition performance. This paper proposes an approach for micro-expression recognition, Spatiotemporal Local Gabor Binary patterns on Three Orthogonal Planes (STLGBP-TOP), by combining spatiotemporal Gabor filtering and LBP-TOP. In this approach, LBP-TOP is employed to encode local dynamic features of micro-expression videos on top of spatiotemporal Gabor filtering. Experimental results on CASME and CASME2 datasets demonstrate the effectiveness of our method. © 2023 SPIE.

关键词： Local binary pattern

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Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation

Deep Spherical Manifold Gaussian Kernel for Unsupervised Dom...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Unsupervised Domain adaptation is an effective method in addressing the domain shift issue when transferring knowledge from an existing richly labeled domain to a new domain. Existing manifold-based methods either are based on traditional models or largely rely on Grassmannian manifold via minimizing differences of single covariance matrices of two domains. In addition, existing pseudo-labeling algorithms inadequately consider the quality of pseudo labels in aligning the conditional distribution between two domains. In this work, a deep spherical manifold Gaussian kernel (DSGK) framework is proposed to map the source and target subspaces into a spherical manifold and reduce the discrepancy between them by embedding both extracted features and a Gaussian kernel. To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss. Extensive experimental results show that DSGK outperforms state-of-the-art methods, especially on challenging cross-domain learning tasks.

关键词： Manifolds Adaptation models computer vision conferences Feature extraction pattern recognition Noise measurement

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Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball

Explaining Classifiers using Adversarial Perturbations on th...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Elliott, Andrew Law, Stephen Russell, Chris Univ Glasgow Turing Inst Glasgow Lanark Scotland UCL Turing Inst London England Amazon Tubingen Tubingen Germany

ISBN: (纸本)9781665445092

We present a simple regularization of adversarial perturbations based upon the perceptual loss. While the resulting perturbations remain imperceptible to the human eye, they differ from existing adversarial perturbations in that they are semi-sparse alterations that highlight objects and regions of interest while leaving the background unaltered. As a semantically meaningful adverse perturbations, it forms a bridge between counterfactual explanations and adversarial perturbations in the space of images. We evaluate our approach on several standard explainability benchmarks, namely, weak localization, insertiondeletion, and the pointing game demonstrating that perceptually regularized counterfactuals are an effective explanation for image-based classifiers.

关键词： Location awareness Bridges computer vision Perturbation methods Neural networks Games Benchmark testing

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