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Vision-Language Models in Remote Sensing: Current Progress and Future Trends

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arXiv 2023年

作者： Li, Xiang Wen, Congcong Hu, Yuan Yuan, Zhenghang Zhu, Xiao Xiang The King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates The Institute of Remote Sensing and Geographic Information Systems Peking University Beijing100871 China Data Science in Earth Observation Technical University of Munich Munich80333 Germany The Munich Center for Machine Learning Munich80333 Germany

The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These models provide intelligent solutions close to human thinking, enabling us to use general artificial intelligence to solve problems in various applications. However, in remote sensing (RS), the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-language models excel, as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. Vision-language models can go beyond visual recognition of RS images, model semantic relationships, and generate natural language descriptions of the image. This makes them better suited for tasks requiring visual and textual understanding, such as image captioning, and visual question answering. This paper provides a comprehensive review of the research on vision-language models in remote sensing, summarizing the latest progress, highlighting challenges, and identifying potential research opportunities. Specifically, we review the application of vision-language models in mainstream remote sensing tasks, including image captioning, text-based image generation, text-based image retrieval, visual question answering, scene classification, semantic segmentation, and object detection. For each task, we analyze representative works and discuss research progress. We also summarize the commonly used remote sensing vision-language datasets, codebases, and online accessible resources. Finally, we delineate the limitations of current studies and suggest potential avenues for future advancements. This review aims to provide a comprehensive review of the current research pro

关键词： Semantics

Physics-aware machine learning Revolutionizes Scientific Paradigm for machine learning and Process-based Hydrology

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arXiv 2023年

作者： Xu, Qingsong Shi, Yilei Bamber, Jonathan Tuo, Ye Ludwig, Ralf Zhu, Xiao Xiang Data Science in Earth Observation Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany School of Engineering and Design Technical University of Munich Munich Germany Hydrology and River Basin Management Technical University of Munich Munich Germany Department of Geography Ludwig-Maximilians-University Munich Germany School of Geographical Sciences University of Bristol United Kingdom

Accurate geoscientific process understanding and water cycle prediction are crucial for addressing scientific and societal challenges associated with the management of water resources. Existing reviews predominantly concentrate on the development of machine learning (ML) in this field, yet there is a clear distinction between process-based hydrology and ML as separate paradigms. Here, we introduce physics-aware ML as a transformative approach to overcome the perceived barrier and revolutionize both fields. Specifically, we present a comprehensive review of the physics-aware ML methods, building a structured community (PaML) of existing methodologies that integrate prior physical knowledge or physics-based modeling into ML. We systematically analyze these PaML methodologies with respect to four aspects: physical data-guided ML, physics-informed ML, physics-embedded ML, and physics-aware hybrid learning. PaML facilitates ML-aided hypotheses, accelerating insights from big data and fostering scientific discoveries. We first conduct a systematic review of hydrology in PaML, including rainfall-runoff hydrological processes and hydrodynamic processes, and highlight the most promising and challenging directions for different objectives and PaML methods. Finally, a new PaML-based hydrology platform, termed HydroPML, is released as a foundation for hydrological applications. HydroPML enhances the explainability and causality of ML and lays the groundwork for the digital water cycle’s realization. The HydroPML platform is publicly available at https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Runoff

Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design

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arXiv 2024年

作者： Weinreich, Jan Karandashev, Konstantin Arrieta, Daniel Jose Arismendi Hermansson, Kersti Anatole von Lilienfeld, O. LausanneCH-1015 Switzerland University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Department of Chemistry-Ångström Laboratory Uppsala University Box 538 Uppsala75121 Sweden Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

We present high-quality reference data for two fundamentally important groups of molecular properties related to a compound's utility as a lithium battery electrolyte. The first one is energy changes associated with charge excitations of molecules, namely ionization potential and electron affinity. They were estimated for 7000 randomly chosen molecules with up to 9 non-hydrogen atoms C, N, O, and F (QM9 dataset) using DH-HF, DF-HF-CABS, PNO-LMP2-F12, and PNO-LCCSD(T)-F12 methods as implemented in Molpro software with aug-cc-pVTZ basis set;additionally, we provide the corresponding atomization energies at these levels of theory, as well as CPU time and disk space used during the calculations. The second one is solvation energies for 39 different solvents, which we estimate for 18361 molecules connected to battery design (Electrolyte Genome Project dataset), 309463 randomly chosen molecules with up to 17 non-hydrogen atoms C, N, O, S, and halogens (GDB17 dataset), as well as 88418 amons of ZINC database of commercially available compounds and 37772 amons of GDB17. For these calculations we used the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) method;we additionally provide estimates of gas-phase atomization energies, as well as information about conformers considered during the COSMO-RS calculations, namely coordinates, energies, and dipole moments. © 2024, CC BY.

关键词： Solvation

Rainfall Prediction using Novel Approach for Enhancing Crop Yield

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Rainfall Prediction using Novel Approach for Enhancing Crop ...

Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Visweswararao Seelam Sriharsha Vikruthi Naresh Kumar Bhagavatham P V V S D Nagendrudu Goda SrinivasaRao Ayyappa Chakravarthi M. Dept of CSE PACE Institute of Technology and Sciences. Dept of CSE B V Raju Institute of Technology Telangana Narsapur Medak India Dept of CSE Vignana bharathi Institute of Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Aditya University Surampalem Dept of CSE KL University Vaddeswaram Guntur Andhra Pradesh Dept of CSE-Data Science KKR & KSR Institute of Technology & Sciences Guntur Andhra Pradesh

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

In India, agriculture is one of the fastest-growing fields to improve the country's economy. Based on several factors like climatic conditions, predicting rainfall in these stages is one of the most significant tasks. In recent years, unexpected and unpredictable weather conditions have been observed in some places. This study mainly focused on developing the Hybrid algorithm (HA) to predict rainfall in the early stages and find accurate weather conditions. The proposed approach also called as Novel Approach that improves the algorithm's strength by adopting the advanced preprocessing and feature extraction technique. The training model random forest (RF) also used to find the accurate patterns from the given dataset samples. The preprocessing technique focused on cleaning and transforming the raw data to remove the noise from the input dataset, improving the data quality. The proposed approach combines Autoencoders and Linear Regression (LR) to accurately predict rainfall. Extensive experiments are carried out to evaluate the performance of the hybrid algorithm using historical meteorological data from multiple regions with varying climatic conditions. The results show that the hybrid algorithm outperforms traditional methods and individual machine-learning models in terms of accuracy, robustness, and generalization.

关键词： Radio frequency Training Rain Accuracy Temperature Autoencoders Linear regression Time series analysis Prediction algorithms data models

Identifying Weight-Variant Latent Causal Models

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arXiv 2022年

作者： Liu, Yuhang Zhang, Zhen Gong, Dong Gong, Mingming Huang, Biwei van den Hengel, Anton Zhang, Kun Shi, Javen Qinfeng Australian Institute for Machine Learning The University of Adelaide Australia School of Computer Science and Engineering The University of New South Wales Australia School of Mathematics and Statistics The University of Melbourne Australia Halicioğlu Data Science Institute University of California San Diego United States Department of Philosophy Carnegie Mellon University United States

The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations. Identifying true latent causal representations from observed data, while allowing instantaneous causal relations among latent variables, remains a challenge, however. To this end, we start with the analysis of three intrinsic indeterminacies in identifying latent space from observations: transitivity, permutation indeterminacy, and scaling indeterminacy. We find that transitivity acts as a key role in impeding the identifiability of latent causal representations. To address the unidentifiable issue due to transitivity, we introduce a novel identifiability condition where the underlying latent causal model satisfies a linear-Gaussian model, in which the causal coefficients and the distribution of Gaussian noise are modulated by an additional observed variable. Under some mild assumptions, we can show that the latent causal representations can be identified up to trivial permutation and scaling. Furthermore, based on this theoretical result, we propose a novel method, termed Structural caUsAl Variational autoEncoder (SuaVE), which directly learns latent causal representations and causal relationships among them, together with the mapping from the latent causal variables to the observed ones. Experimental results on synthetic and real data demonstrate the identifiability and consistency results and the efficacy of SuaVE in learning latent causal representations. Copyright © 2022, The Authors. All rights reserved.

关键词： Gaussian distribution

Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift

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arXiv 2022年

作者： Liu, Yuhang Zhang, Zhen Gong, Dong Gong, Mingming Huang, Biwei van den Hengel, Anton Zhang, Kun Shi, Javen Qinfeng Australian Institute for Machine Learning The University of Adelaide Australia School of Computer Science and Engineering The University of New South Wales Australia School of Mathematics and Statistics The University of Melbourne Australia Halicioğlu Data Science Institute University of California San Diego United States Department of Philosophy Carnegie Mellon University United States

Multi-source domain adaptation (MSDA) addresses the challenge of learning a label prediction function for an unlabeled target domain by leveraging both the labeled data from multiple source domains and the unlabeled data from the target domain. Conventional MSDA approaches often rely on covariate shift or conditional shift paradigms, which assume a consistent label distribution across domains. However, this assumption proves limiting in practical scenarios where label distributions do vary across domains, diminishing its applicability in real-world settings. For example, animals from different regions exhibit diverse characteristics due to varying diets and genetics. Motivated by this, we propose a novel paradigm called latent covariate shift (LCS), which introduces significantly greater variability and adaptability across domains. Notably, it provides a theoretical assurance for recovering the latent cause of the label variable, which we refer to as the latent content variable. Within this new paradigm, we present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable to achieve more nuanced rendering of observational data. We demonstrate that the latent content variable can be identified up to block identifiability due to its versatile yet distinct causal structure. We anchor our theoretical insights into a novel MSDA method, which learns the label distribution conditioned on the identifiable latent content variable, thereby accommodating more substantial distribution shifts. The proposed approach showcases exceptional performance and efficacy on both simulated and real-world datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

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arXiv 2022年

作者： Hedström, Anna Weber, Leander Bareeva, Dilyara Krakowczyk, Daniel Motzkus, Franz Samek, Wojciech Lapuschkin, Sebastian Höhne, Marina M.-C. Understandable Machine Intelligence Lab TU Berlin Berlin10587 Germany Department of Electrical Engineering and Computer Science TU Berlin Berlin10587 Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin10587 Germany Department of Computer Science University of Potsdam Potsdam14476 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus-a comprehensive, evaluation toolkit in Python that includes a growing, wellorganised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/). © 2022, CC BY-NC-SA.

关键词： Python

CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases

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CBAM_SAUNet: A novel attention U-Net for effective segmentat...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Srividya Tirunellai Rajamani Kumar Rajamani Angeline J Karthika R Björn W. Schuller Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany Department of Artificial Intelligence Marwadi University Rajkot Gujarat India Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India CHI - Chair of Health Informatics MRI Technische Universität München (TUM) Germany Munich Data Science Institute Germany Munich Center for Machine Learning and GLAM - the Group on Language Audio & Music Imperial College London London UK

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability. Furthermore, recent research has focused on identification and reporting of corner cases in segmentation to accelerate the utilisation of deep learning models in clinical practise. However, achieving good model performance on such corner cases is a less-explored research area. In this paper, we propose CBAM_SAUNet which enhances the dual attention decoder block of SAUNet to improve its performance on corner cases. We achieve this by utilising a novel variant of the Convolutional Block Attention Module (CBAM)’s channel attention in the decoder block of SAUNet. We demonstrate the effectiveness of CBAM_SAUNet in the Automated Cardiac Diagnosis Challenge (ACDC) cardiac MRI segmentation challenge. Our proposed novel approach results in improvement in the Dice scores of 12% for Left Ventricle (LV) as well as Right Ventricle (RV) segmentation and 8% for Myocardium (MYO) for the identified corner-case dataset.

关键词： Deep learning Image segmentation Attention mechanisms Shape Magnetic resonance imaging Myocardium Decoding Medical diagnostic imaging

Ensuring the data Security and Integrity over Cloud Computing Environment using Novel Cipher Strategy

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Ensuring the Data Security and Integrity over Cloud Computin...

IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Praveen Kumar E B. Yasotha Narmadha PG P. Chandrakala C. Ushapriya G Chamundeeswari Department of CSE (IoT) Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Data science and Business systems SRMIST kattankulathur Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning St. Martin's Engineering College Telangana India Department of Electronics and Communication Engineering Saveetha Engineering college Chennai Tamil Nadu India

The advent of cloud computing has revolutionized the Internet. Users may effortlessly collaborate, back up, and access their information from any location thanks to cloud computing. When it comes to providing IT enabled services via the internet, cloud computing is the clear winner. Cloud computing allows a large number of people to share resources like computers, programs, and servers. Users of cloud services often have no idea where their data actually resides or where it came from. Users of cloud computing services are understandably concerned about the safety of their data once it has been uploaded to the cloud with respect to service providers. Privilege control, comprehensive encryption algorithms, management of keys, provenance instances and global consistency verification are only some of the numerous data privacy and security issues that occur as a result. This study introduces a novel cloud security strategy, Cloud based Novel Cipher Strategy (CNCS), and analyzes issues related to the safety and integrity of data stored in the cloud. We're looking into many data safety related factors here. In this article, we'll look closely at a selection of data protection strategies now in use throughout the globe to guarantee the highest level of security possible. Later, the study delves into the various methods of cloud based data security. To prevent unwanted access, alteration, or deletion of dynamic as well as static information, a procedure for validating their integrity is outlined. The proposed system CNCS efficiency is evaluated in the resulting section through the following metrics such as: integrity level, data storage efficiency and security level. All these metrics are evaluated based on the size of data, in which it is tested with respect to number of days the proposed scheme is tested over the industrial environment to prove the performance and efficiency of the proposed approach.

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