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Utilising SkyScript for Open-Vocabulary Categorization, Extraction, and Captioning to Enhance Multi-Modal Tasks in Remote Sensing

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Remote Sensing in Earth Systems sciences 2024年第3期7卷 149-158页

作者： Nagaraj, Saranya Sivakumar, Shanmuga Priya Annabel, Lawrence Sherly Puspha Joshi, Vilas Ramrao Patil, Mithun Baswaraj Patil, Vishal Ratansing SRM Institute of Science and Technology School of Computing SRM Institute of Science and Technology Department of Artificial Intelligence and Machine Learning St. Joseph’s College of Engineering Department of Computer Engineering ISBM College of Engineering Department of Computer Science and Engg N K Orchid College of Engg and Technology Solapur Department of CSE (AIML) Pimpri Chinchwad College of Engineering Nigdi

The SkyScript dataset was developed by integrating large-scale remote sensing images from Google Earth Engine with geo-tagged semantic data from OpenStreetMap. This open-access dataset, consisting of 2.6 million image-text pairs covering 29,000 unique tags, facilitates various remote sensing tasks such as cross-modal retrieval, image captioning, and classification. The dataset ensures global representation and semantic diversity, although it exhibits a higher concentration of high-resolution images from the USA and Europe due to licencing constraints. The images, sourced from multiple collections with varying ground sampling distances, are paired with captions generated using a combination of rule-based methods and logistic regression models for tag classification. Experiments demonstrate that models pre-trained on SkyScript outperform those trained on other datasets in zero-shot classification and fine-grained attribute recognition, highlighting its potential for advancing vision-language models in remote sensing applications. Future improvements could involve enhancing geographic coverage and refining caption quality using advanced language models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Image captioning Remote sensing Sampling distance Vision language

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Enhancing the Resolution of ChestX-ray Images with SRGAN and Sub-Pixel CNN 7

Enhancing the Resolution of ChestX-ray Images with SRGAN and...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Reddy, K. Lokeshwar Sudharson, S. Natarajan, B. Theetchenya, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science And Engineering Chennai India Computer Science And Engineering Vellore Institute of Technology Chennai India Computer Science And Engineering Sona College of Technology Salem India

ISBN: (纸本)9798350371406

A crucial diagnostic tool for respiratory and heart disorders is chest X-ray imaging. However, the limited resolution of acquired images can hinder accurate interpretation and subsequent treatment decisions. To address this challenge, we employ deep learning-based super-resolution techniques, specifically SRGAN and ESPCN, to enhance the chest X-ray images. In this study, we proposed a modified ESPCN and present our findings on the application of SRGAN and ESPCN for enhancing chest Xray images. The enhanced image resolution enables radiologists to visualize fine details and subtle abnormalities that were previously difficult to discern. This enhancement significantly improves image interpretation and diagnostic accuracy. Moreover, our findings demonstrate the potential of super-resolution techniques for medical education and training. Through extensive experimentation, we saw significant visual improvements, as evidenced by PSNR(peak signal-to-noise ratio) of SRGAN and proposed ESPCN are 40.46 and 40.17, and achieved SSIM values 0.84 and 0.71 respectively. In conclusion, our study showcases the efficacy of SRGAN and ESPCN in enhancing chest X-ray imaging. By improving image quality and diagnostic accuracy, these techniques have the potential to revolutionize medical diagnostics, leading to better patient outcomes and a more effective healthcare system. © 2023 IEEE.

关键词： Image resolution

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A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning

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ACM Transactions on Graphics 2023年第5期42卷 1-16页

作者： Zhang, Yuxin Tang, Fan Dong, Weiming Huang, Haibin Ma, Chongyang Lee, Tong-Yee Xu, Changsheng MAIS Institute of Automation China and School of Artificial Intelligence CAS UCAS China Institute Of Computing Technology CAS China Kuaishou Technology China Department of Computer Science and Information Engineering National Cheng-Kung University Taiwan

This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, that can fit in most existing arbitrary image style transfer models, such as CNN-based, ViT-based, and flow-based methods. As the key component in image style transfer tasks, a suitable style representation is essential to achieve satisfactory results. Existing approaches based on deep neural networks typically use second-order statistics to generate the output. However, these hand-crafted features computed from a single image cannot leverage style information sufficiently, which leads to artifacts such as local distortions and style inconsistency. To address these issues, we learn style representation directly from a large number of images based on contrastive learning by considering the relationships between specific styles and the holistic style distribution. Specifically, we present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components: a parallel contrastive learning scheme for style representation and transfer, a domain enhancement (DE) module for effective learning of style distribution, and a generative network for style transfer. Qualitative and quantitative evaluations show the results of our approach are superior to those obtained via state-of-the-art methods. The code is available at https://***/zyxElsa/CAST_pytorch. © 2023 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Deep learning approach for Parkinson’s screening with geometric features from spiral and wave drawings

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Multimedia Tools and Applications 2025年 1-29页

作者： Malik, Meenakshi Onyema, Edeh Michael Uddin, Mueen Yadav, Poonam Sharma, Aanchal Jose, Jazlyn Shankar, Achyut Alasim, Fahad Abidi, Mustufa Haider BML Munjal University Gurugram India Department of Mathematics and Computer Science Coal City University Enugu Nigeria Adjunct Faculty Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India College of Computing & Information Technology University of Doha for Science and Technology Doha Qatar Starex University Gurugram India School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh201310 India Industrial Engineering Department College of Engineering King Saud University P. O. Box-800 Riyadh11421 Saudi Arabia Advanced Manufacturing Institute King Saud University Riyadh11421 Saudi Arabia

Parkinson’s disease (PD) is the second most common neurological ailment and affects a substantial percentage of the elderly population. A deterioration in handwriting is a common indication of Parkinson’s disease. Researchers have developed an algorithm-based tool to assist in distinguishing between Parkinson’s disease patients and healthy persons because the disease can be difficult to identify. Unfortunately, previous studies have not tackled this problem or adopted a preventive approach. Examining handwriting is one way to detect PD. After analyzing multiple publications, we examined the 2020 and 2022 articles from Convolutional Neural Network utilizing deep learning techniques. Spiral and wave photos of those in good health and those with Parkinson’s illness were employed in our collection. This investigation used the Random Forest, VGG-16, and ResNet-50 approaches. We applied ResNet-50 for the drawings and got 97% accuracy for spiral pictures over 10 epochs and 98% accuracy for wave images over eight epochs by using feature extraction and preprocessing techniques. This exceptional performance demonstrates ResNet-50’s potential as a potent tool for medical diagnostics, utilizing its deep learning skills to generate precise and reliable results compared to existing studies in which ResNet-50 has 83% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition 7

Self-Paced Neutral Expression-Disentangled Learning for Faci...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Wu, Zhenqian Li, Xiaoyuan Ren, Yazhou Pu, Xiaorong Zhu, Xiaofeng He, Lifang University of Electronic Science And Technology of China School of Computer Science And Engineering China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Lehigh University Department of Computer Science And Engineering United States

ISBN: (纸本)9798350359145

The accuracy of facial expression recognition is typically influenced by the following factors: high similarities across different expressions, disturbing factors, and the subtle and rapid micro-movements of the face. One potential solution to overcome these obstacles is to leverage the hidden neutral information present in neutral expression images. In this paper, we propose a Self-Paced Neutral Expression-Disentangled Learning (SPNDL) model, which aims to disentangle the neutral information from facial expressions, facilitating the extraction of crucial features and variations. This approach enables the capture of discriminative details within similar expressions and the detection of micro-facial movements. To enhance the learning of these neutral expression-disentangled features (NDFs) and mitigate the challenges posed by non-convex optimization, we propose a self-paced learning (SPL) strategy based on NDFs during the training phase. SPL learns samples from easy to complex by increasing the number of samples selected into the training process, which enables to effectively suppress the negative impacts caused by low-quality samples and inconsistently distributed NDFs. Experiments on three popular databases (i.e., CK+, Oulu-CASIA, and RAF-DB) show the effectiveness of our proposed method. © 2023 IEEE.

关键词： Face recognition

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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Empowering Semantic Segmentation with Selective Frequency Enhancement and Attention Mechanism for Tampering Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3270-3283页

作者： Xu, Xu Lv, Wenrui Wang, Wei Zhang, Yushu Chen, Junxin Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China

Nowadays, massive amounts of multimedia contents are exchanged in our daily life, while tampered images are also flooding the social networks. Tampering detection is therefore becoming increasingly important for multimedia integrity, and it is generally realized by designing specific convolutional neural networks. From a new perspective, this article proposes two pluggable modules for empowering existing semantic segmentation models for tampering detection. First, a selective frequency enhancement (SFE) module is developed to suppress the semantic information and selectively enhance the tamper information. Second, a boundary enhanced attention (BEA) module is designed to highlight the edge information of tempered area. Our SFE and BEA modules are combined with five mainstream semantic segmentation networks for performance evaluation. The experiment results demonstrate that our modules are able to empower the semantic segmentation networks for tampering detection, and their combinations even perform better than state-of-the-art algorithms in certain datasets. © 2020 IEEE.

关键词： Semantics

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Adaptive Incentivize for Federated Learning with Cloud-Edge Collaboration under Multi-level Information Sharing

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IEEE Transactions on computers 2025年第7期74卷 2445-2460页

作者： Yuan, Shijing Dong, Beiyu Li, Jie Guo, Song Chen, Hongyang Wu, Chentao Wu, Jie Zhao, Wei China Telecom Research Institute Department of Fundamental Network Operations Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Hong Kong University of Science and Technology Department of Computing Hong Kong Zhejiang Lab Hangzhou311121 China Temple University Department of Computer and Information Sciences United States CAS Shenzhen Institute of Advanced Technology Shenzhen518055 China

Federated Learning with Cloud-Edge Collaboration (FL-CEC) has emerged as a cutting-edge paradigm in distributed learning. Efficient resource investment incentive mechanisms are crucial to encouraging clients in FL-CEC to contribute the necessary data and computational resources for training. However, existing studies are inadequate in meeting the incentive design requirements under multi-level information-sharing scenarios. Moreover, current works often rely on specific functional relationships between resource investment and global model accuracy. To bridge these gaps, this paper investigates the incentive problem for data and computational resource investment under multi-level information-sharing levels. We design a resource investment incentive mechanism based on a weighted potential game without depending on any specific functional relationship between data investment and model accuracy. Furthermore, we propose four algorithms to solve resource investment strategies for different levels of information sharing. The complexity and convergence rates of the proposed algorithms are thoroughly analyzed. Finally, we construct a simulation incentive platform on the Aliyun. Extensive evaluations demonstrate that the proposed scheme effectively enhances social welfare, and improves collaborative training accuracy and efficiency. © 1968-2012 IEEE.

关键词： Open access

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An Efficient Approach of Brain Tumor Detection & Extraction using BWT with Auto Enhance Technique 3

An Efficient Approach of Brain Tumor Detection & Extraction ...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Bahadure, Nilesh Bhaskarrao Gonge, Sudhanshu Patni, Jagdish Chandra Shah, Bhoomi Raju, Nagrajan Patil, Prasenjeet D. Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India Maharashtra Pune India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Electrical and Electronics Engineering SASTRA University Tamil Nadu Thanjavur India Department of Computer Science and Engineering MIT ADT University Pune India

Brain tumor detection and extraction of infected areas with utmost accuracy is essential in medical image analysis. Despite ex- cessive research, segmentation operation for brain tumor detection remains an exigent issue because medical images have diverse image contents, non-uniformity object texture, occlusion, image noise, and other factors that consistently influence and affect per- formance analysis. This paper introduces the most efficient brain tumor detection and extraction technique, using Berkely Wavelet Transformation-based segmentation with auto-enhancement techniques. If the input images are too dark or their intensity is not good enough, the auto-enhance technique will automatically adjust the image quality. The extensive experimental analysis and sim- ulation, with a comparison of the results with other state-of-the-art techniques available, prove the significance and effectiveness of the proposed algorithm. The primary intent of this paper is to develop a simple but performance-oriented algorithm for brain tumor detection and extraction, which is suitable for most imaging modalities and gives the best performance even if images are of poor quality. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Image segmentation

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Introducing on-chain graph data to consortium blockchain for commercial transactions

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Frontiers of computer science 2024年第2期18卷 255-257页

作者： Pengyi ZHANG Yuchen YUAN Jie SONG Yu GU Qiang QU Yongjie BAI Software College Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Blockchain Lab Huawei Cloud Computing Technologies Co.Ltd.Shenzhen 518101China

1 Introduction A blockchain can be referred to as a distributed ledger[1].Nowadays,according to our previous work[2],the graphrelated blockchain grows efficiently in both academic studies and commercial *** data,algor... 详细信息

关键词： transactions ledge commercial

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