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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Ar-Manet: A Low-Quality Image Restoration Method Based on Multi-Feature Fusion

SSRN

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SSRN 2024年

作者： Zhang, Yugui Zhou, Qi Jia, Tingting Ke, Zunwang Jia, YiZhen Wang, Gang Yang, Guang Du, Minghua Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Hunan RAMON Science & Technology Co. Ltd. Changsha410138 China Department of Stomatology The First Medical Centre Chinese PLA General Hospital Beijing100853 China School of Software Xinjiang University Urumqi830091 China School of Computing and Data Engineering NingboTech University Ningbo315100 China National Heart and Lung Institute Imperial CollegeLondon LondonSW7 2BX United Kingdom The Royal Brompton Hospital LondonSW3 6NP United Kingdom Medical School of Chinese PLA China

Exposure problems often affect image quality more significantly than noise and blurring, seriously affecting their effectiveness in computer vision applications. To address this core problem, this paper proposes an innovative deep-learning method. The method uses pyramidal multi-scale decomposition to repair the image layer by layer, taking care of both overexposure and underexposure problems. On this basis, we improve the U-Net model by introducing the Strengthen-Operate-Subtract (SOS) feature enhancement module and a multi-scale aggregate back-projection module to achieve finer image restoration and improve the propagation of feature information. In addition, we introduce an atrous residual dense block at the highest scale to expand the receptive field of the network and efficiently capture local information to improve the quality of the exposure-corrected images. These innovations significantly optimize the effect of image exposure correction and improve the problems of loss of image details, oversaturation of regional contrast, and imbalance of color naturalness in existing techniques. Through extensive and in-depth analytical evaluations, we have validated the effectiveness of our model, which also demonstrates excellent performance compared with the most recent methods on the test database. © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

arXiv

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arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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CheXMed: A Multimodal Algorithm to Improve Pneumonia Detection in the Elderly

SSRN

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SSRN 2023年

作者： Hao, Ren Jing, Fengshi Chen, Zhurong Liu, Le Zhou, Jiandong Jing, Ran Lian, Wanmin Tian, Junzhang Xu, Zhongzhi Zhang, Qingpeng Cheng, Weibin Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangzhou510317 China UNC Project-China UNC Global School of Medicine University of North Carolina Chapel HillNC27599 United States College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China College of Foreign Languages Jishou University Zhangjiajie427000 China Nuffield Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom Information Department Guangdong Second Provincial General Hospital Guangzhou510317 China School of Public Health Sun Yat-sen University Guangzhou510080 China School of Data Science City University of Hong Kong Hong Kong

Objectives: Pneumonia can be a deadly illness for particular populations, one of which is older adults. While studies have successfully trained artificial intelligent assisted diagnostic tools to detect pneumonia using chest X-ray images, they were targeted to the general population without stratification on age ***: This study investigated the performance disparities between geriatric and younger patients when using chest X-ray images to detect pneumonia, and developed and tested a multimodal algorithm that incorporates clinical notes together with image data to improve pneumonia detection performance for older *** and Methods: Three most used pneumonia diagnostic models, CheXNet, DenseNet121, and ResNet18, were evaluated to test their performance in older persons. Electronic medical records and frontal-view chest X-ray images of 2,572 inpatients were collected in a local hospital data to train and test a multi-input deep neural network framework named CheXMed, for pneumonia detection among older patients. Accuracy, precision, recall, F1-score, and AUC were used for model performance ***: CheXNet, DenseNet121, and ResNet18 achieved 9.5%, 6.0%, and 6.9% of higher AUC in the younger age group than in the elderly group, respectively. The proposed novel model, CheXMed, outperforms CheXNet by 15.3% in accuracy, 9.0% in precision, 38.3% in recall, and 24.0% in ***: Diagnostic models achieving good performance for young adults or the general population do not guarantee that they are useful for the elderly. Chest X-ray images combined with clinical information significantly improve pneumonia detection performance among elderly patients who are more vulnerable while neglected in general models. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events

arXiv

引用

arXiv 2024年

作者： Wu, Xin Huang, Zhanchao Wang, Li Chanussot, Jocelyn Tian, Jiaojiao The School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China The Key Laboratory of Spatial Data Mining and Information Sharing The Ministry of Education The Academy of Digital China Fuzhou University Fuzhou350108 China The National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Remote Sensing Technology Institute German Aerospace Center Weßling82205 Germany

In large-scale disaster events, the planning of optimal rescue routes depends on the object detection ability at the disaster scene, with one of the main challenges being the presence of dense and occluded objects. Existing methods, which are typically based on the RGB modality, struggle to distinguish targets with similar colors and textures in crowded environments and are unable to identify obscured objects. To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities. Based on these datasets, we propose a multimodal collaboration network for dense and occluded vehicle detection, MuDet for short. MuDet hierarchically enhances the completeness of discriminable information within and across modalities and differentiates between simple and complex samples. MuDet includes three main modules: Unimodal Feature Hierarchical Enhancement (Uni-Enh), Multimodal Cross Learning (Mul-Lea), and Hard-easy Discriminative (He-Dis) Pattern. Uni-Enh and Mul-Lea enhance the features within each modality and facilitate the cross-integration of features from two heterogeneous modalities. He-Dis effectively separates densely occluded vehicle targets with significant intra-class differences and minimal inter-class differences by defining and thresholding confidence values, thereby suppressing the complex background. Experimental results on two re-labeled multimodal benchmark datasets, the 4K-SAI-LCS dataset, and the ISPRS Potsdam dataset, demonstrate the robustness and generalization of the MuDet. The codes of this work are available openly at https://***/Shank2358/MuDet. © 2024, CC BY-NC-SA.

关键词： Remote sensing

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Capturing Doublet Intermediate Emitters by Chemically Crosslinking Confinement towards Spatiotemporal Encryption

引用

Angewandte Chemie 2023年第1期136卷

作者： Haomin Li Dr. Huanyu Lei Dr. Shudeng Ma Dr. Tianfu Song Prof. Dr. Yan Li Prof. Dr. Haifeng Yu School of Materials Science and Engineering Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University Beijing 100191 China School of Materials Science and Engineering Key Laboratory of Polymer Chemistry and Physics of Ministry of Education Peking University Beijing 100871 China Contribution: Conceptualization (lead) Data curation (lead) Software (lead) Writing - original draft (lead) South China Advanced Institute for Soft Matter Science and Technology School of Molecular Science and Engineering South China University of Technology Guangzhou 510641 China Contribution: Data curation (supporting) Software (supporting) Contribution: Conceptualization (supporting) Data curation (supporting) Formal analysis (supporting) Contribution: Conceptualization (supporting) Formal analysis (supporting) Writing - review & editing (supporting) Contribution: Supervision (equal) Writing - review & editing (equal)

Photoluminescence is one of the most meticulous ways to manipulate light energy. Typical photoluminescent emitters are mostly stable substances with a pure photophysical process of spontaneous photon-emission from their excited states. Intermediate emitters are elusive attributing to their synchronous energy transfer process including photophysical and incomplete photochemical pathways. An intermediate emitter containing radicals is more difficult to be observed due to its inherent chemical reactivity. Here, these challenges are overcome by spontaneously formed space limitations in polymer crosslinking networks meanwhile chemically active intermediates are captured. These doublet intermediates exhibit unique long-wavelength emissions under chemically crosslinking confinement conditions, and their luminous mechanism provides a novel perspective for designing intermediate emitters with liquid-crystal character and photoresponsive features towards spatiotemporal encryption, promising for the detection of photochemical reactions and the development of fascinating luminescent systems.

关键词： Aggregation Encryption Liquid Crystal Network Luminescence Radical Intermediate

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Multimodal Large Language Model with LoRA Fine-Tuning for Multimodal Sentiment Analysis

引用

ACM Transactions on Intelligent Systems and Technology 1000年

作者： Jie Mu Wei Wang Wenqi Liu Tiantian Yan Guanglu Wang School of Data Science and Artificial Intelligence Dongbei University of Finance and Economics China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University China the National and Local Joint Engineering Laboratory of Computer Aided Design Dalian University China School of Software Dalian University of Technology China

Multimodal sentiment analysis has become a popular research topic in recent years. However, existing methods have two unaddressed limitations: (1) they use limited supervised labels to train models, which makes it impossible for model to fully learn sentiments in different modal data; (2) they employ text and image pre-trained models trained in different unimodal tasks to extract different modal features, so that the extracted features cannot take into account the interactive information between image and text. To solve these problems, in this paper we propose a Vision-Language Contrastive Learning network (VLCLNet). First, we introduce a pre-trained Large Language Model (LLM), which is trained from vast quantities of multimodal data, has better understanding ability for image and text contents, thus being effectively applied to different tasks while requiring few amount of labelled training data. Second, we adapt a Multimodal Large Language Model (MLLM), BLIP-2 (Bootstrapping Language-Image Pre-training) network, to extract multimodal fusion feature. Such MLLM can fully consider the correlation between images and texts when extracting features. In addition, due to the discrepancy between the pre-training task and the sentiment analysis task, the pre-trained model will output the suboptimal prediction results. We use LoRA (Low-Rank Adaptation) fine-tuning strategy to update the model parameters on sentiment analysis task, which avoids the issue of inconsistent task between pre-training task and downstream task. Experiments verify that the proposed VLCLNet is superior to other strong baselines.

关键词： Deep learning Large language model Multimodal representation learning Sentiment analysis

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

引用

arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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