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On Energy-aware and Verifiable Benchmarking of Big Data processing.targeting AI Pipelines

On Energy-aware and Verifiable Benchmarking of Big Data Proc...

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2024 ieee International conference on Big Data, BigData 2024

作者： Theodorou, Georgios Karagiorgou, Sophia Kotronis, Christos Ubitech Ltd Limassol Cyprus

ISBN: (纸本)9798350362480

As Artificial Intelligence (AI) is revolutionizing various industries and applications, understanding the hardware requirements and energy consumption of AI pipelines in Big Data (BD) applications has become increasingly essential. This paper presents a comprehensive, scalable framework, designed to systematically measure hardware resources, energy usage, and model performance across two prominent data modalities: tabular data and images. The framework is generalizable, facilitating replicability across the AI research community, and encourages the deployment of AI models with comprehensive metrics beyond traditional accuracy, promoting the optimization of pipelines for real-world scenarios. Through detailed benchmarking, we identify EfficientNet as a standout model for image classification, and XGBoost for tabular data, both excelling in their respective domains. Notably, our findings show that Graphics processing.Units (GPUs) account for approximately 90% of total energy consumption in image-based tasks, while Central processing.Units (CPUs) are responsible for around 50% of energy use in tabular data processing. The merit of our innovative proposed framework combines information theory and probability theory to enhance our understanding of AI model performance in Edge-to-Cloud (E2C) applications that demand efficient Big Data processing.in distributed environments. By seamlessly integrating energy efficiency with hardware optimization, it enables realtime monitoring of energy consumption and computing resources in containerized environments, providing precise insights for optimizing AI workloads. This framework facilitates scalable AI deployment on resource-constrained edge devices, reducing energy consumption while enhancing AI model robustness and interpretability, thereby promoting greater trust and transparency in AI-powered decision-making for critical real-world applications. This emphasizes the importance of multi-objective optimization for more sustainabl

关键词： Graphics processing.unit

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Feature Selection Using Evolutionary Techniques

Feature Selection Using Evolutionary Techniques

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2023 ieee International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gholami, Mandana Mouhoub, Malek Sadaoui, Samira University of Regina Dept. of Computer Science ReginaSKS4S 0A2 Canada

ISBN: (纸本)9798350337020

Data clustering has many applications in machine learning, data mining and image processing. K-means is the most popular clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has limitations, such as large feature spaces, which may affect its effectiveness. To improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) evolutionary technique to select the most relevant features of datasets. We conducted several experiments to compare our approach with other methods, such as PCA and Particle Swarm Optimization (PSO). The results demonstrate the effectiveness of BBO for feature selection. © 2023 ieee.

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Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval

Comprehensive Visual Features and Pseudo Labeling for Robust...

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2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Ngo, Bach Hoang Nguyen, Dat Thanh Do-Tran, Nhat-Tuong Huy Thien, Phuc Pham An, Minh-Hung Nguyen, Tuan-Ngoc Hoang, Loi Nguyen Dinh Nguyen, Vinh Dinh, Quang-Vinh Ai Vietnam Research Viet Nam Fpt Telecom Viet Nam Ho Chi Minh City University of Technology Faculty of Computer Science and Engineering Viet Nam Eastern International University School of Computing and Information Technology Viet Nam

ISBN: (纸本)9798350302493

Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle tracking are the main components of most vision-based vehicle recovery systems. Unfortunately, the limited amount of information provided by traffic video feeds limits the vision-based vehicle retrieval algorithm's efficacy. Therefore, this article proposes a contrastive cross-modal vehicle retrieval approach to maximize the complementarity of natural language and visual representations. An efficient method to fuse multiple input image features is also proposed to extract comprehensive information from various vehicles along with pseudo labeling and efficient post-processing.techniques to enhance retrieval accuracy. The proposed method achieved the 3rd ranking of Mean Reciprocal Rank (MRR) score of 0.4795 on the test set for the Challenge Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions 2023. Source code for the proposed approaches is openly accessible at https://***/anminhhung/AICity-2023-Track2. © 2023 ieee.

关键词： image enhancement

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Application of subspace-based CNN speech noise reduction model in medical field

Application of subspace-based CNN speech noise reduction mod...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Fu, Xian Dong, Sisi Zhou, Weiqi Zhang, Zhuzhu Ye, Ruiwen Ren, Ting School of Computer and Information Engineering Hubei Normal University Huangshi435000 China

ISBN: (纸本)9781510666351

In order to enhance the noise reduction capability and recognition efficiency of speech recognition technology applied in medical devices, this paper adopts an improved subspace CNN based noise reduction model, adding a subspace projection module with two orthogonal self-attentive mechanisms to the original model, inputting the feature vectors extracted from the convolutional layer to the self-attentive layer, and the two self-attentive layers learn and project orthogonally to each other to obtain the noise embedding vector and speech embedding vectors to extract cleaner speech. In this paper, the improved subspace CNN noise reduction model is compared with the CNN noise reduction model by comparing the distortion degree, segmental signal-to-noise ratio and quality at different signal-to-noise ratios to demonstrate the reliability of the model. The results show that the overall distortion level of the model is reduced by about 7.27%, the overall segmental SNR performance is increased by about 15.27% and the quality is improved by about 8.7%, which is a certain improvement in performance compared with the traditional CNN noise reduction model. © 2023 SPIE.

关键词： Vectors

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Lightweight target detection method based on improved YOLOv5

Lightweight target detection method based on improved YOLOv5

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Feng, Jianxin Zhou, Minghao Hao, Enguang Pan, Chengsheng Liaoning Key Laboratory of Communication Network Liaoning Dalian116622 China College of Information Engineering Dalian University Liaoning Dalian116622 China

ISBN: (纸本)9781510666351

Target detection is one of the hot research issues in the field of computer vision in recent years. Large scale convolution networks can effectively improve the accuracy of target detection, but they are not suitable for application scenarios with limited computing and storage capacity. In order to solve the above problems, this paper proposes a lightweight target detection network based on improved YOLOv5 - CS-YOLOv5 network. This network uses a lightweight network ShuffleNetv2 to reconstruct backbone, effectively reducing the network complexity, and adds a CA attention module to the feature extraction network to enhance the network feature expression ability, and solves the problem of neuron deactivation through H-Swish activation function. The final model size was compressed to 1.64MB, and the mAP50 on the COCO test set reached 47.6%, reducing the model size by 13.7% and improving the detection accuracy by 1.9% compared with the original YOLOv5 model. © 2023 SPIE.

关键词： image processing

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In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient computer Vision 48

In-Sensor & Neuromorphic Computing Are all You Need for Ener...

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48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Datta, Gourav Liu, Zeyu Kaiser, Md Abdullah-Al Kundu, Souvik Mathai, Joe Yin, Zihan Jacob, Ajey P. Jaiswal, Akhilesh R. Beerel, Peter A. University of Southern California United States Information Sciences Institute United States Intel Labs United States

ISBN: (纸本)9781728163277

Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing.is efficient using SNNs, the data transfer between the image sensors and the downstream processing.costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing.by 12-96× and the resulting total energy by 2.32× compared to traditional CV processing. with a 3.8% reduction in accuracy on imageNet. © 2023 ieee.

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computer Vision-Based Detection and recognition Model of Objects for any Autonomous Systems in Low Illumination Condition 2

Computer Vision-Based Detection and Recognition Model of Obj...

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2nd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Ashok, P. Kavinilavu, A. Ananthi, S. Suganthi, P. Iniyan, E.U. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India 5Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665437899

The theories and methods it studies have received extensive attention in many disciplines and fields, promoting the development of artificial intelligence systems and expanding the possibilities of computer applications. image processing.is an important field of pattern recognition methods. The widely used character recognition (OCR) is a typicalapplication of pattern recognition in image processing. The pattern recognition process can be seen as a mapping process from the sample space to the category space. If you divide an n-dimensional feature space with n features asparameters into different regions, each region corresponds to a class of pattern classes. Among them, feature selection and extraction is an important part of pattern recognition. Ifthe selected features can fully reflect the essential features of the class, the classifier is easier to design. According to the presence or absence of standard samples, pattern recognition can be divided into surveillance identification methods and non-supervisory identification methods. The supervisory identification method is to design a classifierunder the condition that the class to which the training sample belongs is known, and the method for recognizing the sample is performed by the classifier. © 2022 ieee.

关键词： Character recognition

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A Paradigm Shift towards computer Vision

A Paradigm Shift towards Computer Vision

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2023 ieee International conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Chaithra, N. Jha, Janhvi Sayal, Anu Gupta, Veethika Gupta, Ashulekha Karnataka Bangalore India Taylor's University Department of Mathematics Malaysia Doon University School of Social Sciences Department of Economics Uttarakhand India Department of Management Uttarakhand Dehradun India

ISBN: (纸本)9781665474924

In today's world, machine learning, artificial intelligence, IoT, deep learning and several other techniques have become the need of the moment. One such division of artificial intelligence is computer vision. The main goal of computer vision development is to create paradigms for extracting data and information from images. It has various applications in the fields of industry, agriculture, automations, healthcare, e-commerce and much more. The study examines the most recent events and conceptual frameworks governing the progress of computer vision, with a focus on pattern recognition and image processing. using a variety of applications from the t field. This article attempts to discuss the most current results and applications in computer vision. © 2023 ieee.

关键词： computer vision

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RTrPPG: An Ultra Light 3DCNN for Real-Time Remote Photoplethysmography

RTrPPG: An Ultra Light 3DCNN for Real-Time Remote Photopleth...

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

作者： Botina-Monsalve, D. Benezeth, Y. Miteran, J. Univ Bourgogne Franche Comte ImViA EA7535 Besancon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The acquisition of remote photoplethysmography (rPPG) signals is important in multiple applications. Recently, deep-learning-based approaches such as 3D convolutional networks (3DCNNs) have outperformed traditional hand-crafted methods. However, despite their robust modeling ability, it is well known that large 3DCNN models have high computational costs and may be unsuitable for real-time applications. In this paper, we propose a study of the 3DCNN architecture, finding the best compromise between heart rate measurement precision and inference time. The fast inference is obtained decreasing the input size while the precision performance is obtained introducing a new time and frequency-based loss function by adding the signal-to-noise-ratio component to the regular Pearson's correlation loss function. In addition, changing the input color space from RGB to YUV also improved heart rate measurement precision. Using the VIPL-HR database, we retained the HR mean absolute error at 3.99 bpm which is comparable to 3.87 bpm of the state-of-the-art, while the GPU and CPU inference process improved around 88% from 51.77 ms to 2.32 ms in GPU and from 241.57 ms to 28.65 ms in CPU. The resulting network is called Real-Time rPPG (RTrPPG). We release the RTrPPG source code to encourage reproducibility(1).

关键词： Time-frequency analysis Three-dimensional displays image color analysis Computational modeling Graphics processing.units Photoplethysmography Real-time systems

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Unpaired Face Restoration via Learnable Cross-Quality Shift

Unpaired Face Restoration via Learnable Cross-Quality Shift

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

作者： Dong, Yangyi Zhang, Xiaoyun Wang, Zhixin Zhang, Ya Chen, Siheng Wang, Yanfeng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face restoration aims to recover high-quality (HQ) face images from low-quality (LQ) ones with various unknown degradations. Unpaired face restoration approaches focus on the adaptation to unseen degradations, which is a more challenging setting. Recently, generative facial priors of StyleGAN are used to improve the restoration capability of paired face restoration methods. For unpaired methods, however, using face priors is a challenge due to the lack of paired supervision. To address this issue, we take advantage of the editing capabilities of StyleGAN's latent code and propose a novel learnable cross-quality shift. The proposed learnable cross-quality shift not only introduces the generative facial priors into the unpaired framework, but also enables the straight-forward addition/subtraction in the latent feature space to achieve quality conversion. Furthermore, we design a two-branch framework with the proposed cross-quality shift to deal with unpaired data and improve the fidelity of restoration. With the unpaired framework, our method can be fine-tuned on images with unseen degradation. Experimental results show that (i) compared to state-of-the-art methods, our method improves performances under moderate and severe degradation situations;and (ii) both the proposed learnable cross-quality shift and the two-branch framework benefit the restoration performance.

关键词： Degradation computer vision Codes Face recognition conferences image restoration Faces

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