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Transformer for Single image Super-Resolution

Transformer for Single Image Super-Resolution

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

作者： Lu, Zhisheng Li, Juncheng Liu, Hong Huang, Chaoyan Zhang, Linlin Zeng, Tieyong Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Single image super-resolution (SISR) has witnessed great strides with the development of deep learning. However, most existing studies focus on building more complex networks with a massive number of layers. Recently, more and more researchers start to explore the application of Transformer in computer vision tasks. However, the heavy computational cost and high GPU memory occupation of the vision Transformer cannot be ignored. In this paper, we propose a novel Efficient Super-Resolution Transformer (ESRT) for SISR. ESRT is a hybrid model, which consists of a Lightweight CNN Backbone (LCB) and a Lightweight Transformer Backbone (LTB). Among them, LCB can dynamically adjust the size of the feature map to extract deep features with a low computational costs. LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA). Extensive experiments show that ESRT achieves competitive results with low computational cost. Compared with the original Transformer which occupies 16,057M GPU memory, ESRT only occupies 4,191M GPU memory. All codes are available at https://***/luissen/ESRT.

关键词： computer vision Adaptation models Computational modeling Superresolution Memory management Graphics processing.units Transformers

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Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations

Speed Is All You Need: On-Device Acceleration of Large Diffu...

引用

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

作者： Chen, Yu-Hui Sarokin, Raman Lee, Juhyun Tang, Jiuqiang Chang, Chuo-Ling Kulik, Andrei Grundmann, Matthias Google LLC 1600 Amphitheatre Parkway Mountain ViewCA94043 United States

ISBN: (纸本)9798350302493

The rapid development and application of foundation models have revolutionized the field of artificial intelligence. Large diffusion models have gained significant attention for their ability to generate photorealistic images and support various tasks. On-device deployment of these models provides benefits such as lower server costs, offline functionality, and improved user privacy. However, common large diffusion models have over 1 billion parameters and pose challenges due to restricted computational and memory resources on devices. We present a series of implementation optimizations for large diffusion models that achieve the fastest reported inference latency to-date(under 12 seconds for Stable Diffusion 1.4 without INT8 quantization for a 512 × 512 image with 20 iterations) on GPU-equipped mobile devices. These enhancements broaden the applicability of generative AI and improve the overall user experience across a wide range of devices. © 2023 ieee.

关键词： Graphics processing.unit

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Revisiting Frequent (Closed) Gradual Itemsets Mining 36

Revisiting Frequent (Closed) Gradual Itemsets Mining

引用

36th ieee International conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Lonlac, Jerry Tchide, Bernoulli Fotsing Bomgni, Alain Doniec, Arnaud Nguifo, Engelbert Mephu IMT Nord Europe IMT University of Lille Centre for Digital Systems Lille France University of Dschang Department of Computer Science Dschang Cameroon Oglala Lakota College Maths Science and Technology Department 490 Piya Wiconi Road KyleSD57752 United States University Clermont Auvergne Clermont Auvergne INP CNRS ENSMSE INP LIMOS Clermont Ferrand France

ISBN: (纸本)9798331527235

The task of mining gradual itemsets holds significant importance in pattern mining, particularly when working with numerical data. It involves the discovery of covariations between attributes in the form of 'The more/less X,..., the more/less Y,' referred to as gradual itemsets. However, discovering these itemsets remains challenging, partly due to the exponential combinatorial search space involved in large-scale data processing. Consequently, existing algorithms for gradual itemset mining encounter difficulties, such as slow processing.speeds, and occasional failures to terminate due to the overwhelming number of candidate itemsets requiring exploration. A large number of candidates is generated, but a large proportion of them turns out to be infrequent once their supports are computed. This paper introduces an approach to streamline this process by efficiently reducing the number of candidates for which support needs to be computed through the introduction of a stricter upper-bound criterion. By circumventing the costly support computation for numerous candidate itemsets, our approach exhibits efficiency in terms of speed when applied to real databases, including large-scale databases that pose challenges for existing algorithms. Furthermore, we establish a connection in terms of pattern coverage between the two principal gradualness semantics commonly employed in the literature. © 2024 ieee.

关键词： Candidate pruning Data Mining Gradual itemsets Upper bound

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image Feature processing.Based on Fuzzy recognition 2

Image Feature Processing Based on Fuzzy Recognition

引用

2nd International conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2022

作者： Ni, Guibo Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (纸本)9781510657717

Machine vision inspection combines a fast and accurate computer with image processing.technology, which is of great help to improve the ability of intelligent image recognition. The ultimate goal of the recognition and classification system is to identify the detected defects quickly and accurately. The defects with obvious characteristics can be identified in a short time due to their great differences. Due to the imperfect recognition technology and the low robustness of the algorithm, the detection is inaccurate and the difficulty of the recognition effect is increased. In this paper, based on the targets with similar characteristics, the membership function of the target to be recognized is constructed after extracting the characteristic parameters of the recognized target. The characteristic function of the fuzzy set is established to calculate the membership degree of the target sample belonging to the fuzzy set, and finally, the classification of the characteristic sample is recognized according to the maximum membership principle. The experimental results show that the method of this paper is based on pattern recognition and has good results, which has guiding significance for all kinds of image feature recognition and processing.and will greatly reduce the intensity and workload of manual processing. © 2022 SPIE. All rights reserved.

关键词： Membership functions

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Biometric-Based Access Control Systems with Robust Facial recognition in IoT Environments 3

Biometric-Based Access Control Systems with Robust Facial Re...

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3rd ieee International conference on Intelligent Techniques in Control, Optimization and Signal processing. INCOS 2024

作者： Satish Babu, B.V. Manoranjini, J. Changala, Ravindra Aarif, Mohd Mary, S. Suma Christal Raj, I Infant Prasad v Potluri Siddhartha Institute of Technology Department of It Vijayawada India Chaitanya Bharathi Institute of Technology Department of Ai & Ml Hyderabad India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Associate Editor-Publishing Global Research Network Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

The integration of biometric-based access control systems with robust facial recognition technology within Internet of Things (IoT) environments holds paramount significance in addressing contemporary security challenges. The novelty of the research endeavours to contribute to this critical area by investigating and implementing a novel approach, employing Convolutional Neural Networks (CNN) for facial classification within the biometric framework. The significance of this study lies in its potential to enhance security measures in IoT environments, ensuring reliable and efficient access control. Previous attempts at integrating facial recognition into IoT have encountered notable challenges, including suboptimal accuracy, vulnerability to adversarial attacks, and scalability issues. This research builds upon these prior issues by introducing CNN for facial classification, leveraging its capabilities in image processing.and pattern recognition to achieve enhanced accuracy and resilience against potential security threats. The novelty of the study is underscored by the application of CNN within the biometric context of IoT, offering a cutting-edge solution tailored to the intricacies of interconnected devices. Experiments conducted on benchmark datasets showcase the promising capabilities of the proposed system, with face recognition accuracies reaching an impressive 98%. These results underscore the potential of this research to significantly advance the state-of-the-art in biometric-based access control, particularly within the context of IoT environments. The utilization of CNN introduces a pioneering element, contributing to the evolution of secure and efficient access control solutions in the era of IoT. © 2024 ieee.

关键词： Biometrics

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Integrating dual attention with TransUNet for esophagus image segmentation

Integrating dual attention with TransUNet for esophagus imag...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Yu, Zhehao Jiang, Shaochen Wang, Liejun College of Computer Science and Technology Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510680425

Esophageal cancer is a common digestive tract tumor with a high mortality rate. Currently, the identification of esophageal carcinoma predominantly hinges on thoracic computed tomography scanning. Because the area of the esophagus on CT images is small and belongs to the small target region, the traditional image segmentation network is difficult to accurately segment the tumor area of esophageal cancer. Although the traditional U-Net architecture and Transformer integrated variants perform well in medical segmentation tasks, they lack image localization and channel feature extraction capabilities. To address the aforementioned issues, this manuscript proposes a novel medical image segmentation model, christened DAA-TransUNet. We propose a new module called Dual Attention Area (DAA) module,it can extract image position and channel features. And we combine the DAA module and the Efficient Channel Attention (ECA) module into a new module, which we call the Efficient Channel Dual Attention (ECDA) module. DAA-TransUNet integrates Transformer and ECDA modules into a traditional U-shaped architecture, enabling it to extract not only global and local information, but also image position and channel features. We have verified the superior performance of our network by performing numerous experiments on our collected esophageal dataset. We used DAA-TransUNet to perform experiments on our collected dataset, and the average DSC(%) reached 75.26% and the average HD95(mm) reached 3.54. © 2024 SPIE.

关键词： image segmentation

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Synthesizing CTA image Data for Type-B Aortic Dissection using Stable Diffusion Models 46

Synthesizing CTA Image Data for Type-B Aortic Dissection usi...

引用

46th Annual International conference of the ieee Engineering in Medicine and Biology society, EMBC 2024

作者： Abaid, Ayman Ali Farooq, Muhammad Hynes, Niamh Corcoran, Peter Ullah, Ihsan University Hospital Galway University of Galway Newcastle Road Ireland University of Galway School of Computer Science Ireland University of Galway School of Engineering College of Science and Engineering Ireland University of Galway Insight Sfi Research Center for Data Analytics Ireland

ISBN: (纸本)9798350371499

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to image (T2I) stable diffusion model. The results demonstrate that the optimized T2I CTA diffusion model successfully render images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions. © 2024 ieee.

关键词： computerized tomography

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Multiview heterogeneous graph representation learning with fusion of high-order information and low-order information

Multiview heterogeneous graph representation learning with f...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Sun, Dengdi Meng, Yundong Luo, Bin Ding, Zhuanlian School of Artificial Intelligence Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

ISBN: (纸本)9781510680425

Graph neural networks (GNNs) are extensively employed in the analysis of data structured as graphs and excel at learning intricate node relationships. However, traditional GNN methods encounter difficulties when applied to analyzing heterogeneous graphs (HGs) with diverse node types and relationships, due to the complex heterogeneity and semantics of the data. Therefore, we propose Multi-view Heterogeneous Graph Representation Learning with Fusion of High-Order Information and Low-Order Information(MHGRL). MHGRL produces node embeddings by integrating node content transformation, meta-path-based intra- and inter-aggregation, as well as the amalgamation of low- and high-order information from multiple perspectives. Comprehensive experiments on three diverse heterogeneous graph datasets reveal the significant benefits of our MHGRL in node classification endeavors. © 2024 SPIE.

关键词： Contrastive Learning

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Adaptive division of multilayer feature fusion for Detailed 3D reconstruction

Adaptive division of multilayer feature fusion for Detailed ...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Li, Saiya Jiang, Guoqing Zhang, Zongliang Su, Jinhe JiMei University The School of Computer Engineering Xiamen China

ISBN: (纸本)9781510680425

Point cloud is a versatile and easily accessible lightweight data representation, is widely used in visual ***, the discrete nature of 3D objects, along with the challenges posed by noise interference and uneven density, contribute to the complexity of accurately reconstructing 3D *** to only using global features for 3D reconstruction makes the result surface difficult to describe the target details, and only using local features for 3D reconstruction makes the result surface *** this paper, we propose a method of adaptive division and multilayer feature fusion to reconstruction the three-dimensional shapes with unoriented point *** adjusted the octree division rule based on neural network to adaptively divide the target point cloud with a more reasonable division *** addition, we inherit the moving least squares method based on the octree and transform the discrete point set into a three-dimensional *** difference is that we combine the octree features of different depths to obtain the context information of the shape in a local-plus-global *** conduct comparative experiments with other learning-based methods on ShapeNet dataset, our method outperforms advanced learning-based surface reconstruction approaches, shows superior quantitative and qualitative results. © 2024 SPIE.

关键词： image reconstruction

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Predicting blood pressure from the change of photoplethysmography features

Predicting blood pressure from the change of photoplethysmog...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Chen, Qin Yang, Xuezhi Chen, Yawei Wang, Dingliang School of Computer and Information Hefei University of Technology Hefei230601 China Anhui Province key Laboratory of Industry Safety and Emergency Technology Hefei230601 China Department of Electronic Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9781510680425

Blood pressure (BP) is a crucial indicator for assessing cardiovascular health, and effective monitoring contributes to early treatment of cardiovascular diseases. Photoplethysmography (PPG) has shown immense potential in non-invasive BP monitoring. However, existing studies often overlook the impact of feature variations on BP changes. Therefore, this study propose a blood pressure estimation method based on PPG feature changes. Initially, high-quality PPG segments are obtained through signal processing.from PPG waveforms, and 68 contributory PPG differential features (DF) for BP changes are derived through differencing. Subsequently, optimal subsets of DFs are identified using correlation analysis and stepwise forward feature selection. Finally, an interpretable model is employed to map DF to BP changes, incorporating calibration values for accurate BP estimation. The proposed method is evaluated on self-collected datasets. The root mean square error (RMSE) for systolic blood pressure (SBP) is 8.39 mmHg, and for diastolic blood pressure (DBP), it is 5.75 mmHg. The corresponding Pearson correlation coefficient (PCC) values are 0.88 and 0.83. Correlation analysis indicates a close relationship between PPG differential features and BP changes. Compared to direct use of PPG features, the RMSE of BP estimation improves by approximately 40%, highlighting the effectiveness of PPG differential features in BP estimation. © 2024 SPIE.

关键词： Blood pressure

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