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

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PMoET: Going Wider Than Deeper Using the Parallel Mixture of Experts Transformer for 3D Hand Gesture recognition 4th

PMoET: Going Wider Than Deeper Using the Parallel Mixture of...

引用

4th International conference on Intelligent Systems and pattern recognition-ISPR

作者： Alboody, Ahed Slama, Rim CESI CESI LINEACT Lab Equipe Accueil UR 7527 Paris France

ISBN: (纸本)9783031821554;9783031821561

Mistral AI (Artificial Intelligence) Startup has released Mixtral-Transformers based on a Mixture of Experts layer (MoE). MoE architectures have gained prominence in both Large Language Modeling (LLM) and computer vision due to their ability to scale efficiently by dynamically selecting an ensemble of specialized sub-models (a group of experts) for different inputs rather than using the entire model for every input to optimize the overall performance with a constant computational cost. Following Mixtral Transformers, GSHard, GLaM, and Switch Transformer, we propose the "Parallel Mixture-of-Experts Transformer (PMoET)" that coupled with Spatial Linear Layer (SLL) and use parallel Mixture of Experts (MoE) layers for going wider instead of deeper in traditional Transformer. PMoET model could be scaled along width using parallel MoE layers instead of a single Feed-Forward Network (FFN) layer in traditional Transformer. Using MoE architecture for graph learning of 3D dynamic skeleton-data for 3D hand gesture recognition can provide significant benefits due to the model's ability to handle complex and varied 3D skeleton data efficiently. For this purpose, Decoupling spatial and temporal graph learning using a PMoET model by integrating parallel MoE layers into parallel configuration is an advanced approach for recognizing 3D hand gestures. By focusing on both the spatial configuration of the hand and the temporal dynamics of gestures, the PMoET architecture provides a robust and efficient solution for complex gesture recognition tasks. Finally, to evaluate the PMoET performance, conducting extensive experiments on SHREC'17 dataset provides strong evidence of PMoET effectiveness. PMoET improves the overall performance, the training stability and reduces the computational cost. Experiments show the efficiency and superiority of the PMoET model variants, particularly PMoET Model 4, which surpasses the state-of-the-art (SOTA) performance achieved by DG-STA method.

关键词： Mixture-of-Experts (MoE) Parallel MoE Transformer Accelerating Training 3D Hand Gesture recognition Human-computer Interaction

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Surface deformation monitoring based on DINSAR technique

Surface deformation monitoring based on DINSAR technique

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2023 International conference on pattern recognition, Machine vision and Intelligent Algorithms, PRMVIA 2023

作者： Yu, Xianhui Yu, Peng Xia, Le He, Yuanrong Xiamen University of Technology College of Computer and Information Engineering Xiamen China Ministry of Natural Resources Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area Technology Innovation Center for Ecological Conservation and Restoration in Dongting Lake Basin Changsha China

ISBN: (纸本)9798350346596

In this paper, we monitor the surface deformation of Helan Mountains by using the DInSAR (Differential Interferometric Synthetic Aperture Radar) technology and Sentinel-1 SAR data from December 2019 to December 2021. The surface deformation of the Helan Mountain National Natural Reserve with a study area extending to 1935 km2 are observed. The findings indicate that the surface of Helan Mountain Reserve is rising in the east and sinking in the west, with no obvious increasing tendency in the north or south of Helan Mountain. Additionally, the vertical deformation map created by D-InSAR processing is used to monitor two monitoring cycles with significant deformations in June 2020 and December 2021. Furthermore, Helan Mountain has experienced two earthquakes with magnitudes of 3 or greater, according to the differential interference technique. An important decision-making basis for disaster prevention and mitigation can be provided by the deformation data of the ground surface obtained by the InSAR technology. © 2023 ieee.

关键词： Decision making

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A Novel Classification Model Based on DWT and CNN-LSTM Motor EEG Imagination Signals 7

A Novel Classification Model Based on DWT and CNN-LSTM Motor...

引用

7th International conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Yang, Yuyao Chen, Zuowen Li, Weisu Ma, Yahong Xijing University School of Electronic Information Xi'an Key Laboratory of High Pricision Industrial Intelligent Vision Measurement Technology Xi'an China

ISBN: (纸本)9798350350890

Feature extraction and pattern classification play important roles in brain-computer interface (BCI) systems, which are widely used in rehabilitation medicine, artificial intelligence and other fields. Although advanced EEG acquisition technology can generate a large amount of EEG data for different brain regions, it inevitably has disadvantages such as high cost, long time consuming and inherent high false positives. Considering the contradiction between data volume and accuracy, this paper proposes a MI EEG feature extraction and classification method based on discrete wavelet transform (DWT) and Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Based on the project BCI motor imagination EEG(MI-EGG) data set, all evaluation indexes of the proposed CNN-LSTM model are superior to other models. The prediction accuracy, precision, recall rate, F1-score and MCC of C3 and C4 channels reaches 96.43%, 96.57%, 96.57%, 96.57% and 93.02%, respectively. The reliability of the proposed model is also proved by tenfold cross-validation. The average accuracy is 99.64%, which is far higher than the other models. It can be concluded that it is a reliable method to predict MI-EEG signals by using CNN-LSTM to train the modeling ability of images. © 2024 ieee.

关键词： Electroencephalography

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Research on Detection and recognition System of Oil and Gas Pipeline Weld Seam Based on Machine vision Technology 3

Research on Detection and Recognition System of Oil and Gas ...

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3rd International conference on computer vision and pattern Analysis, ICCPA 2023

作者： Zhang, Liyuan Zhou, Desheng Liu, Liming Liu, Xiaobin Yuan, Tao Zhao, Ming Ren, Kai Xi’an Shiyou University Shaanxi Xi'an China The 11th Oil Production Plant of PetroChina Changqing Oilfield Company Gansu Qingyang China

ISBN: (纸本)9781510667563

As the construction of oil and gas pipelines continues to advance, the safety of pipelines has garnered increasing attention. Pipeline welds represent a crucial factor in ensuring the safe transportation of pipelines over the long term. Therefore, to guarantee the quality and safety of pipeline welding, it is necessary to inspect the welds of oil and gas pipelines. Currently, manual detection is the most common method for inspection. However, as the number of pipeline detection points continues to increase, manual detection has revealed issues such as low detection efficiency and the susceptibility of detection results to subjective judgments. To address these shortcomings, this study introduces machine vision technology to the detection and recognition of oil and gas pipeline welds, which holds significant guidance implications for the detection and recognition of oil and gas pipeline welds. © 2023 SPIE.

关键词： Welds

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A Fast Identification Method for Morchella Growth Stage Based on vision Transformer 2

A Fast Identification Method for Morchella Growth Stage Base...

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2nd ieee International conference on Control, Electronics and computer Technology, ICCECT 2024

作者： Ning, Jia Chunjun, Zheng Dalian Neusoft University of Information Intelligent Management Center Dalian China School of Software Dalian Neusoft University of Information Dalian China

ISBN: (纸本)9798350380958

We introduce a rapid identification method for morchella. Based on the unique biological characteristics and growth environmental requirements of morchella, the efficient and accurate identification of key growth stages of morchella is achieved through the integration of information acquisition technology. In order to further improve recognition accuracy, the Multi Stage vision Enhanced Position Encoding vision Transformer (MS-EP ViT) model is adopted. By introducing multi-stage input embedding, enhanced position encoding, and optimized Transformer Encoder layers, the performance of the model in identifying different growth stages of Morchella mushrooms is significantly improved. The experimental results show that the model has achieved significant improvements in recognition accuracy and stability, and the rationality of hyperparameter settings has been verified through convergence and parameter sensitivity experiments. Overall, we provide a more accurate and efficient identification method for monitoring the growth of Morchella, which helps to better understand the growth of Morchella and provides scientific basis for optimizing its growth environment. © 2024 ieee.

关键词： Signal encoding

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Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and computer vision Systems

Big Data and Deep Learning in Smart Cities: A Comprehensive ...

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ieee SoutheastCon conference on Engineering the Future

作者： Adewopo, Victor Elsayed, Nelly Elsayed, Zag Ozer, Murat Zekios, Constantinos L. Abdelgawad, Ahmed Bayoumi, Magdy Univ Cincinnati Sch IT Cincinnati OH 45221 USA Florida Int Univ Dept Elect & Comp Engn Miami FL USA Cent Michigan Univ Sch Engn & Tech Mt Pleasant MI USA Univ Louisiana Dept Elect & Comp Engn Lafayette LA USA

ISBN: (纸本)9798350317107;9798350317114

In the dynamic urban landscape, where interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This dataset is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

关键词： Traffic Surveillance Accident Detection Action recognition Smart City Autonomous Transportation

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NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Metho...

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

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Chen, Liangyu Chu, Xiaojie Yu, Wenqing Jin, Kai Wei, Zeqiang Guo, Sha Yang, Angulia Zhou, Xiuzhuang Guo, Guodong Dai, Bin Peng, Feiyue Xiao, Huaxin Yan, Shen Liu, Yuxiang Cai, Hanxiao Cao, Pu Nie, Yang Yang, Lu Song, Qing Hu, Xiaotao Xu, Jun Xu, Mai Jing, Junpeng Deng, Xin Xing, Qunliang Qiao, Minglang Guan, Zhenyu Guo, Wenlong Peng, Chenxu Chen, Zan Chen, Junyang Li, Hao Chen, Junbin Li, Weijie Yang, Zhijing Li, Gen Li, Aijin Sun, Lei Zhang, Dafeng Liu, Shizhuo Zhang, Jiangtao Qu, Yanyun Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Liang, Qiaohui Lin, Jianxin Wang, Yijun Yin, Lianying Zhang, Rongju Zhao, Wei Xiao, Peng Xu, Rongjian Zhang, Zhilu Zuo, Wangmeng Guo, Hansheng Gao, Guangwei Zeng, Tieyong Pi, Huicheng Zhang, Shunli Kim, Joohyeok Kim, HyeonA Park, Eunpil Sim, Jae-Young Zhai, Jucai Zeng, Pengcheng Liu, Yang Ma, Chihao Huang, Yulin Chen, Junying Natl Univ Defense Technol Changsha Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW 2006 Australia Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland MEGVII Technol Beijing Peoples R China Peking Univ Beijing Peoples R China Bigo Technol Pte Ltd Singapore Singapore Beijing Univ Posts & Telecommun Smart Healthcare Innovat Lab Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China Natl Univ Defense Technol Coll Syst Engn Changsha Peoples R China Natl Univ Defense Technol Coll Liberal Arts & Sci Changsha Peoples R China Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Vis Lab Beijing Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China Nankai Univ Sch Stat & Data Sci Tianjin Peoples R China Beihang Univ Beijing Peoples R China Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China Guangdong Univ Technol Guangzhou Guangdong Peoples R China Tencent OVBU Wuhu Peoples R China SRC B Beijing Peoples R China Xiamen Univ Xiamen Peoples R China Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Harbin Inst Technol Harbin Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan South Korea Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan South Korea Beijing Jiaotong Univ Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. This challenge has 1 track aiming at the stereo image super-resolution problem under a standard bicubic degradation. In total, 238 participants were successfully registered, and 21 teams competed in the final testing phase. Among those participants, 20 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Degradation computer vision conferences Superresolution Benchmark testing pattern recognition Image restoration

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Pc Automation Using Hand Gestures

Pc Automation Using Hand Gestures

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2023 International conference on computer Communication and Informatics, ICCCI 2023

作者： Purushotham, P. Bennet, Ben Sujin Vijayakarthik, P. Kiran, Ajmeera Ramakrishna Pravallika, M. Shivani, Y. Chetan, Ch. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India University of Technology and Applied Sciences Engineering Department Oman R.L.Jalappa Institute of Technology Dodabalapur Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348217

The human computer interaction platform can be implemented in a variety of different ways because to the fact that webcams and other equipment, such as sensors, are both relatively inexpensive and readily available on the market. The use of gestures as a mode of communication between humans and machines is widely regarded as the most effective approach. The employment of hand gestures as a means of communication between humans and machines or computers is particularly helpful in achieving higher levels of information conveyance. The use of hand gestures as a form of nonverbal communication is something that can be put to use in a variety of contexts. Applications that make use of hand gestures have acquired a variety of alternative methodologies, including those that are backed by sensor technology and computer vision, according to research and survey reports. Research is expanding into new territory, particularly in the areas of pattern recognition and gesture recognition. Gestures of the hand can add a new layer of complexity to the operation of contactless computers. As a result, in this project, we produce software that shows a prototype of a system that is able to automatically recognize gestures and executes pc commands in response to certain motions. This software is shown as part of this project. Python is the language that we are utilizing to construct our solution, and we are taking advantage of integrated modules such as cv2 (OpenCV, or open source computer vision, is a collection of programming functions that is primarily geared toward real-time computer vision.) & Mediapipe (detects hand and fingers data points.) © 2023 ieee.

关键词： Human computer interaction

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The Weaknesses of Adversarial Camouflage in Overhead Imagery

The Weaknesses of Adversarial Camouflage in Overhead Imagery

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ieee Applied Imagery pattern recognition Workshop (AIPR)

作者： Van Etten, Adam IQT Labs Arlington VA 22201 USA

ISBN: (纸本)9781665477291

Machine learning is increasingly critical for analysis of the ever-growing corpora of overhead imagery. Advanced computer vision object detection techniques have demonstrated great success in identifying objects of interest such as ships, automobiles, and aircraft from satellite and drone imagery. Yet relying on computer vision opens up significant vulnerabilities, namely, the susceptibility of object detection algorithms to adversarial attacks. In this paper we explore the efficacy and drawbacks of adversarial camouflage in an overhead imagery context. While a number of recent papers have demonstrated the ability to reliably fool deep learning classifiers and object detectors with adversarial patches, most of this work has been performed on relatively uniform datasets and only a single class of objects. In this work we utilize the VisDrone dataset, which has a large range of perspectives and object sizes. We explore four different object classes: bus, car, truck, van. We build a library of 24 adversarial patches to disguise these objects, and introduce a patch translucency variable to our patches. The translucency (or alpha value) of the patches is highly correlated to their efficacy. Further, we show that while adversarial patches may fool object detectors, the presence of such patches is often easily uncovered, with patches on average 24% more detectable than the objects the patches were meant to hide. This raises the question of whether such patches truly constitute camouflage. Source code is available at https://***/IQTLabs/camolo.

关键词： computer vision

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Fog vision for Roads and Rails

Fog Vision for Roads and Rails

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2025 ieee International Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Atti, Surat Pyari Pranitha, A. Prakash Raj, M. New Horizon College of Engineering Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798331529833

Reduced visibility due to fog creates hazardous conditions in sectors like transportation, aviation, and outdoor navigation. This research introduces a dual-purpose monitoring system that combines fog detection with real-time object recognition, designed using a Raspberry Pi 3 Model B+ paired with an OV5647 5MP IR cut camera sensor and QuickDetect, a real-time object detection module utilizing Python and the Ultralytics YOLO v8 model [1] [2]. Our system processes video frames to assess fog presence through contrast and blurriness indicators via OpenCV while simultaneously detecting objects with YOLO v8, enabling a comprehensive environmental assessment. The unified design of fog detection and object recognition on a single, affordable platform allows the system to evaluate visibility and detect relevant obstacles, enhancing situational awareness. Preliminary testing has indicated promising results, with efficient detection of fog and accurate identification of objects. This project contributes a practical and accessible solution for safety improvements in low-visibility environments, with potential applications in smart transportation, environmental monitoring, and autonomous navigation. © 2025 ieee.

关键词： Air navigation

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