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Clustering-based Image-Text Graph Matching for Domain Generalization 27th

Clustering-based Image-Text Graph Matching for Domain Genera...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Park, Nokyung Chae, Daewon Shim, Jeongyong Kim, Sangpil Kim, Eun-Sol Kim, Jinkyu Department of Computer Science and Engineering Korea University Seoul Korea Republic of Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Artificial Intelligence Application Hanyang University Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of

ISBN: (纸本)9783031781919

Learning domain-invariant visual representations is important to train a model that can generalize well to unseen target task domains. Recent works demonstrate that text descriptions contain high-level class-discriminative information and such auxiliary semantic cues can be used as effective pivot embedding for domain generalization problems. However, they use pivot embedding in a global manner (i.e., aligning an image embedding with sentence-level text embedding), which does not fully utilize the semantic cues of given text description. In this work, we advocate for the use of local alignment between image regions and corresponding textual descriptions to get domain-invariant features. To this end, we first represent image and text inputs as graphs. We then cluster nodes within these graphs and match the graph-based image node features to the nodes of textual graphs. This matching process is conducted both globally and locally, tightly aligning visual and textual semantic sub-structures. We experiment with large-scale public datasets, such as CUB-DG and DomainBed, and our model achieves matched or better state-of-the-art performance on these datasets. The code is available at: https://***/noparkee/Graph-Clustering-based-DG. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Multimodality Frequency Feature Customized Learning for Pediatric Ventricular Septal Defects Identification

Multimodality Frequency Feature Customized Learning for Pedi...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Jin, Feifei Zhao, Cheng Yang, Peng Xiang, Zhuo Chen, Xunyi Zhang, Yu Fan, Shumin Zhou, Luyao Chen, Weiling Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen China Ultrasound Department Shenzhen Children Hospital Shenzhen China

ISBN: (纸本)9783031845246

Ventricular septal defects (VSD) can be more effectively identified by combining anatomical structural features from 2D grayscale images and blood flow information from Doppler images. Most current algorithms only perform the same operation on features and do not take into account that different frequency features focus on the expression of different information. Starting from the perspective of frequency and multi-modality, this paper designs a method called multimodality frequency feature customized learning (MFCL) for the identification of VSD. Specifically, this paper first constructs a frequency decomposition module (FD) based on the Fourier transform to extract high-frequency and low-frequency features corresponding to different modes. Secondly, this paper designs a cross-pooling fusion Transformer (CPFT) module tailored for multimodality low-frequency features, which can achieve the fusion of multi-modality global information while reducing computational costs. This paper designs a cross-convolution fusion (CCF) module tailored for multi-modality high-frequency features to achieve the fusion of anatomical structure detail features of 2D grayscale images and blood flow information of color Doppler images. Experimental results show that the proposed algorithm is superior to the comparison methods for VSD identification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image coding

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A Review on Synergizing Convolutional and Swin Transformer Approaches for Superior Remote Sensing Detection

A Review on Synergizing Convolutional and Swin Transformer A...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Allipurapu Meghanath Reddy Sai Likhith Gangadhara R. Ravinder Reddy E. Padmalatha Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Remote sensing object detection plays a crucial role in environmental monitoring, yet traditional methods often face difficulties with varying object scales, intricate backgrounds, and densely populated small objects. This research study elaborates on various methodologies carried out in the remote sensing detection area, with the support of the latest advances in traditional and deep learning techniques. We make a critical presentation of CNNs, Swin Transformers, and hybrid models, their adaptations, benefits, and limitations. Performance metrics along with adaptability across different datasets and environments are presented through comparative analysis of various models. The paper provides in-depth analysis into the ever-changing landscape of remote sensing technologies and the latest tangible applications regarding such technologies.

关键词： YOLO Training Adaptation models Reviews Computational modeling Neural networks Transformers Real-time systems Remote sensing Optimization

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Sparse Context Transformer for Few-Shot Object Detection 21st

Sparse Context Transformer for Few-Shot Object Detection

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Jiu, Mingyuan Mei, Jie Sahbi, Hichem Jiang, Xiaoheng Xu, Mingliang School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Zhengzhou China National SuperComputing Center in Zhengzhou Zhengzhou China Sorbonne University CNRS LIP6 Paris75005 France

ISBN: (纸本)9789819601240

Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for the fine-tuned models to effectively identify new classes in the target domain, particularly when the underlying labeled training data are scarce. In this paper, we devise a novel sparse context transformer (SCT) that effectively leverages object knowledge in the source domain, and automatically learns a sparse context from only few training images in the target domain. As a result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluated the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive performance compared to the related state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Industry 5.0 in Healthcare: An Integrated Framework for Human-Centered Prosthetics Design and Manufacturing 6

Industry 5.0 in Healthcare: An Integrated Framework for Huma...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Ambrogio, Giuseppina Borgia, Carmine Bortolini, Marco Catapano, Gerardo Conforti, Domenico De Napoli, Luigi Gagliardi, Francesco Galizia, Francesco Gabriele Guido, Rosita Longo, Francesco Mirabelli, Federica Mirabelli, Giovanni Mundo, Domenico Regattieri, Alberto Saffioti, Maria Rosaria Scordamaglia, Gabriele Solina, Vittorio Umbrello, Domenico Veltri, Pierpaolo University of Calabria Department of Mechanical Engineering Energy Engineering and Management Engineering Italy University of Bologna Department of Industrial Engineering Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Engineering Italy

Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing, machine learning, digital twins, and tissue engineering have shown promise in prosthetics innovation, their integration remains a significant challenge. This paper presents a novel multidisciplinary framework developed through the National Centre for High-Performance Computing (CN-HPC) project, which addresses the gap between individual technological capabilities and their coordinated application in personalized prosthetics development. The framework, implemented through collaboration between six research groups from two universities, employs the House of Quality methodology to align technological integration with patient needs. Our approach encompasses a comprehensive seven-step process, from initial patient requirements definition through virtual modeling, production process optimization, virtual sensing, machine learning enhancement, digital twin simulation, to advanced tissue engineering. A data model facilitates seamless information exchange between different technological domains and research groups. This research contributes to the field by providing a scalable, human-centered framework for technology integration in medical device development, potentially extending beyond prosthetics to other healthcare applications. The proposed framework represents a significant step toward realizing the full potential of Industry 5.0 in healthcare manufacturing through the systematic integration of advanced technologies for delivering personalized prosthetic solutions. © 2024 The Authors. Published by Elsevier B.V.

关键词： Tissue engineering

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Deduplication and Approximate Analytics for Encrypted IoT Data in Fog-Assisted Cloud Storage 24th

Deduplication and Approximate Analytics for Encrypted IoT ...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Rongxi Ha, Guanxiong Jia, Chunfu Li, Ruiqi Su, Zhen The College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China The College of Safety Science and Engineering Civil Aviation University of China Tianjin China

ISBN: (纸本)9789819615476

With the advancement of the Internet of Things (IoT) technologies, there has been a rapid increase in the volume of IoT data, leading to escalating costs in storage, transmission, and analytics. The benefits of conventional data deduplication schemes are diminishing when applied to IoT data that is similar but distinct, necessitating the development of new approaches to accommodate these new scenarios. This paper proposes a deduplication and approximate analytics scheme for encrypted IoT data in fog-assisted cloud storage. The scheme is based on Generalized Deduplication (GD), Message Locked Encryption (MLE), Homomorphic Encryption (HE), and ciphertext conversion techniques. We employ GD to divide similar but distinct IoT data into bases and deviations, and perform deduplication on the encrypted base to achieve efficient storage while protecting data privacy. Additionally, we utilize Hybrid Homomorphic Encryption (HHE) techniques to convert the symmetric ciphertext of IoT data into homomorphic ciphertext, facilitating approximate analytics while ensuring privacy protection of IoT data in fog-assisted cloud storage and reducing the computation overhead on IoT devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud storage

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Optimizing Early Detection of Lung Disease Through Data-Driven Approaches

Optimizing Early Detection of Lung Disease Through Data-Driv...

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international conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： S. Balapriya Anandaraj B Sathish Kumar P. J Surendran R K S Balamurugan Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Lung illness is any condition that impairs the lungs' capacity to function normally; diabetes, in particular, can have a variety of effects on the lungs and increases the risk of respiratory infections such as pneumonia and TB in those who have it. According to recent studies, 384 million individuals have chronic obstructive pulmonary disease (COPD), and 3 million of them pass away each year as a result of delayed diagnosis. Early detection of any disease can lessen its severity, which will lower the death rate and number of lung disease sufferers. Used deep learning methods for lung detection diagnostics, such as improved fish bee (IFB) and chaotic bird swam optimization (CBSO), despite the prediction rate's high false rate and lack of accuracy. Introduced a technique using machine learning methods, such as the Principal Component Analysis (PCA) algorithm for extraction of features, the Decision Tree approach for sample categorization, and the Support Vector Machine (SVM) for very accurate early illness diagnosis. The results of the suggested approach for early lung tumor identification with a low incorrect rate showed an accuracy of 98.07.

关键词： Support vector machines Accuracy Machine learning algorithms Pulmonary diseases Lungs Medical services Prediction algorithms Diabetes Decision trees Principal component analysis

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital 6

Value Stream Mapping for Smart Pharmaceutical Management in ...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electronic health record

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NETrack: A Lightweight Attention-Based Network for Real-Time Pose Tracking of Nasal Endoscope Based on Endoscopic Image 21st

NETrack: A Lightweight Attention-Based Network for Real-Tim...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Shao, Liangjing Chen, Benshuang Chen, Xinrong Academy for Engineering and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory for Medical Imaging Computing and Computer Assisted Intervention Fudan University Shanghai200032 China

ISBN: (纸本)9789819601271

Vision-based real-time pose tracking of endoscope is significant for navigation and automation of endoscopy. In this work, a deep learning-based framework is proposed for vision-based real-time pose tracking of nasal endoscope based on endoscopic images. Specifically, an attention-based module is introduced into the feature encoder to extract and integrate multi-dimensional and multi-scale information. Furthermore, a novel pose encoder is proposed to extract more complicated representation from the feature map for pose vector prediction. The proposed method is compared with state-of-the-art methods on the dataset, which consists of endoscopic videos and endoscope poses obtaining by an optical tracking system. The experimental results demonstrate that the proposed network achieves better performance than state-of-the-art works for real-time nasal endoscope pose estimation. The average tracking error of our method is 5.78 mm, while the relative translation error and rotation error are respectively 0.48 mm and 0.22 degree on average. In addition, the average inference speed of our network is 46 fps, which achieves the real-time requirement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Endoscopy

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Advanced Content Classification and Topic Modelling Techniques for Dark Web Exploration

Advanced Content Classification and Topic Modelling Techniqu...

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advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Jatin Kumar Joy Khaneja Arpit Tyagi Shalu Manav Rachna University Haryana India Manav Rachna University Faridabad Haryana India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The Dark Web, an entirely concealed segment of the internet, poses significant challenges in cybersecurity due to its anonymity and the illicit activities it harbors. This study addresses content classification techniques, topic modeling techniques related to Dark Web crawling, and their application roles in finding and analyzing potential harmful content. To understand its application in topic modeling techniques, including text-based and multimedia content classification, and more complex algorithms such as LDA, it would require a detailed literature review. Subsequently, delineate the advantages and disadvantages of various strategies for addressing difficulties specific to the Dark Web, including encrypted data, evolving obfuscation techniques, and the widespread dissemination of disinformation. These findings emphasize that constant innovation in machine learning and NLP techniques is necessary to further increase the effectiveness of Dark Web monitoring. Therefore, this research could have ramifications for both cybersecurity experts and law enforcement agencies by giving them deeper insights into emerging threats and thus providing better strategies to mitigate associated risks. The study indicates potential future research avenues in machine learning, the integration of decentralized technology, and the formulation of ethical frameworks to ensure responsible analysis of Dark Web content.

关键词： Technological innovation Ethics Dark Web Content management Law enforcement Computational modeling Machine learning Cryptography computer security Monitoring

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