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Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects

IEEE Access 2025年 13卷 95753-95796页

作者： Awal Ahmed Fime Saifuddin Mahmud Arpita Das Md. Sunzidul Islam Jong-Hoon Kim School of Computing Southern Illinois University Carbondale IL USA Department of Computer Science and Information Systems Bradley University Peoria IL USA Department of Computer Science and Engineering The University of Texas at Arlington Arlington TX USA Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh Department of Computer Science Advanced Telerobotics Research Laboratory Kent State University Kent OH USA

Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, graph-based methods, and interactive scene generation. Evaluation metrics such as Fréchet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation.

关键词： Autoencoders Image reconstruction Transformers Training Three-dimensional displays Diffusion models Visualization Surveys Image synthesis Decoding

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Efficient Hierarchical Exploration with An Active Subgoal Generation Strategy

Efficient Hierarchical Exploration with An Active Subgoal Ge...

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IEEE International Conference on Robotics and Biomimetics

作者： Xin Xu Guoyu Zuo Jiangeng Li Gao Huang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China Faculty of Information Beijing University of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China

ISBN: (纸本)9781665481106

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach in long-horizon and complex tasks. It has been successfully used by decomposing tasks via subgoals. However, the existing goal-conditioned HRL methods often suffer from training inefficiency in the long-horizon tasks with sparse external rewards. For this problem, this paper proposes a novel framework that can train high-level policy efficiently with an effective sub goal generation method. The key component of the framework is an active subgoal generation strategy which consists of (a) an efficient high-level policy learning mechanism and (b) two measures of novelty and coverage for filtering effective subgoals. We also introduce the HER mechanism to the framework to solve the problem of sparse reward in the environment. Building upon the proposed framework, this paper develops an active hierarchical exploration strategy with an efficient subgoal generation method. We test our method on a variety of continuous control tasks. Experimental results demonstrate that the strategy outperforms the state-of-the-art HRL approaches.

关键词： Training Learning systems Representation learning Filtering Biomimetics Buildings Reinforcement learning

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IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical Layer Jamming Attacks

arXiv

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arXiv 2024年

作者： Huang, Huan Zhang, Hongliang Cai, Yi Zhang, Yunjing Lee Swindlehurst, A. Han, Zhu Electronic Information School Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston United States Center for Pervasive Communications and Computing University of California Irvine United States

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a "disco" IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are ineffective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Ray-Tracing based CDL Modeling for C-V2X Scenarios with Multiple Roadside Vehicles

Ray-Tracing based CDL Modeling for C-V2X Scenarios with Mult...

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IEEE International Conference on Electronic information and Communication Technology (ICEICT)

作者： Yiran Wang Danping He Hao Duo Hao An Ke Guan Dao Tian Andrej Hrovat School of Electronic and Information Engineering Beijing Jiaotong University Beijing China CTTL-Terminal China Academy of Information and Communication Technology Beijing China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation Shenzhen China Department of Communication Systems Jožef Stefan Institute Ljubljana Slovenia Jožef Stefan International Postgraduate School Ljubljana Slovenia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

Cellular vehicle-to-everything (C-V2X) communication has provided safer and more convenient interactions between vehicles and surroundings. To support the improvement of intelligent C-V2X communication, it is essential to understand the characteristics of the wireless channel. In this paper, channel measurement is performed in the urban C-V2X scenario at 5.915 GHz. The material electromagnetic (EM) parameters are calibrated based on the measured results, which are used for multipath data obtaining in ray-tracing (RT) simulation. After that, the clustered delay line (CDL) models for the C-V2X scenario are proposed to study the spatial characteristics of the significant multipath components. Related cluster and in-cluster parameters are extracted and analyzed based on CDL modeling. This paper provides useful calibrated EM parameters for extensive simulations. The complex channel data in the C-V2X scenarios with and without multiple roadside vehicles are effectively simulated and studied. The proposed CDL models can offer guidance for channel modeling research in the C-V2X scenario.

关键词： Wireless communication Base stations Simulation Ray tracing Delay lines information and communication technology Delays

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Bimodal person recognition using dorsal-vein and finger-vein images 24

Bimodal person recognition using dorsal-vein and finger-vein...

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24th KES International Conference on Knowledge-Based and Intelligent information and Engineering systems, KES 2020

作者： Abbes, Amal Trabelsi, Randa Boukhris Ayed, Yassine Ben Multimedia InfoRmation systems andAdvanced Computing Laboratory MIRACL University of Sfax Tunisia University of Sfax Sfax Engineering School BP W Sfax3038 Tunisia

Nowadays, human recognition with biometric characteristic have been investigatedin many researchers. However, traditional biometric methods are not always robust againstcounterfeit and spoof attacks. The challenge ofthis work is to develop a bimodal biometric system, based on morereliable and robust characteristics against counterfeiting. Indeed, biometric system based on the venousnetwork of the hand gives higher recognition rate compared to other systems. Furthermore, the fusion of two prints minimums the errorrate and counterfeits. In our biometric system, the most challenging phase is the feature extraction step. For this, we propose a new feature extraction approach based on the concatenation of the pyramid of Difference of Gaussian (DoG) and Local Line Binary Patterns (LLBP) histograms (H-DoG_LLBP). To evaluate the proposed system, we optedfor Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers. The experimentalstudy is based on the dorsal hand vein BOSPHORUS database and finger veinMMCBNU_6000 database. Our system presents an area under curve (AUC) equal to 0.99, and 0.001 mean square error (MSE) with ANN, and 0.0042equal error rate(EER) with SVM. © 2020 The Authors. Published by Elsevier B.V.

关键词： Support vector machines

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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Long-term cybersecurity applications enabled by quantum networks

arXiv

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arXiv 2023年

作者： Peters, Nicholas A. Alshowkan, Muneer Chapman, Joseph C. Pooser, Raphael C. Rao, Nageswara S.V. Newell, Raymond T. Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods for Engineered Systems Section Oak Ridge National Laboratory Oak RidgeTN37831 United States MPA-Quantum Los Alamos National Laboratory Los AlamosNM87545 United States

If continental-scale quantum networks are realized, they will provide the resources needed to fulfill the potential for dramatic advances in cybersecurity through quantum-enabled cryptography applications. We describe recent progress and where the US is headed as well as argue that we go one step further and jointly develop quantum and conventional cryptography methods for joint deployments along the quantum backbone infrastructure. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum cryptography

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Opportunities for Retrieval and Tool Augmented Large Language Models in Scientific Facilities

arXiv

引用

arXiv 2023年

作者： Prince, Michael H. Chan, Henry Vriza, Aikaterini Zhou, Tao Sastry, Varuni K. Dearing, Matthew T. Harder, Ross J. Vasudevan, Rama K. Cherukara, Mathew J. Advanced Photon Source Argonne National Laboratory IL United States Center for Nanoscale Materials Argonne National Laboratory IL United States Argonne Leadership Computing Facility Argonne National Laboratory IL United States Business and Information Systems Argonne National Laboratory IL United States Center for Nanophase Materials Oak Ridge National Laboratory Oak RidgeTN United States

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output. © 2023, CC BY.

关键词： Microelectronics

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Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

arXiv

引用

arXiv 2023年

作者： Chen, Jinbiao Zhang, Zizhen Cao, Zhiguang Wu, Yaoxin Ma, Yining Ye, Te Wang, Jiahai School of Computer Science and Engineering Sun Yat-sen University China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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