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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： Ahmed Fime, Awal Mahmud, Saifuddin Das, Arpita Islam, Md. Sunzidul Kim, Jong-Hoon Southern Illinois University School of Computing CarbondaleIL62901 United States Bradley University Department of Computer Science and Information Systems PeoriaIL61625 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States Khulna University of Engineering and Technology Department of Computer Science and Engineering Khulna9203 Bangladesh Kent State University Advanced Telerobotics Research Laboratory Department of Computer Science KentOH44240 United States

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. © 2013 IEEE.

关键词： Deep learning

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Persona Inconstancy in Multi-Agent LLM Collaboration: Conformity, Confabulation, and Impersonation

arXiv

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arXiv 2024年

作者： Baltaji, Razan Hemmatian, Babak Varshney, Lav R. Department of Electrical and Computer Engineering Beckman Institute for Advanced Science And Technology University of Illinois Urbana-Champaign United States

Multi-agent AI systems can be used for simulating collective decision-making in scientific and practical applications. They can also be used to introduce a diverse group discussion step in chatbot pipelines, enhancing the cultural sensitivity of the chatbot’s responses. These applications, however, are predicated on the ability of AI agents to reliably adopt assigned personas and mimic human interactions. To see whether LLM agents satisfy these requirements, we examine AI agent ensembles engaged in cross-national collaboration and debate by analyzing their private responses and chat transcripts. Our findings suggest that multi-agent discussions can support collective AI decisions that more often reflect diverse perspectives, yet this effect is tempered by the agents’ susceptibility to conformity due to perceived peer pressure and occasional challenges in maintaining consistent personas and opinions. Instructions that encourage debate in support of one’s opinions rather than collaboration increase the rate of inconstancy. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs or more realistic simulations of group decision-making may remain untapped. © 2024, CC BY.

关键词： Chatbots

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Focused Bidirectional Search Trees: A Bidirectional Optimal Fast Matching Method for Robot Path Planning

Focused Bidirectional Search Trees: A Bidirectional Optimal ...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Li, Chenming Wang, Jiankun Meng, Max Q.-H. The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Southern University of Science and Technology Jiaxing Research Institute Jiaxing China University of Alberta Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350327182

Robot path planning in high-dimensional continuous space is of great significance in robotics. The conventional bidirectional fast marching method utilizes brute-force search to explore the space, which is computationally expensive. Though improving the planning efficiency, heuristic-based bidirectional fast marching search results in a suboptimal path under a predetermined number of random samples. To address these issues, we propose Focused Bidirectional Fast Marching Trees (FBFMT*), developing a novel vertex exploring sequence to constrain the forward and backward searches within the brute-force searched regions. It enables the search trees to meet in the middle of the solution path, which guarantees the optimality under the current set of samples and searches faster than the bidirectional brute-force strategy. Extensive simulations in different scenarios validate the superiority of the proposed method, showing that FBFMT∗ can speed up the planning process and find a better solution. We also extend the proposed method to general conditions so that the forward and backward searches of FBFMT∗ can meet at any portion of the path. FBFMT∗ reveals that the heuristic-based bidirectional fast marching search strategy can be a general and efficient technique for robot path planning in high-dimensional continuous spaces. © 2023 IEEE.

关键词： Robot programming

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Volumetric B1+ field homogenization in 7 Tesla brain MRI using metasurface scattering

arXiv

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arXiv 2024年

作者： Yoon, Gyoungsub Yu, Sunkyu Lee, Jongho Park, Namkyoo Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Laboratory for Imaging Science and Technology Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of

Ultrahigh field magnetic resonance imaging (UHF MRI) has become an indispensable tool for human brain imaging, offering excellent diagnostic accuracy while avoiding the risks associated with invasive modalities. When the radiofrequency magnetic field of the UHF MRI encounters the multifaceted complexity of the brain—characterized by wavelength-scale, dissipative, and random heterogeneous materials—detrimental mesoscopic challenges such as B1+ field inhomogeneity and local heating arise. Here we develop the metasurface design inspired by scattering theory to achieve the volumetric field homogeneity in the UHF MRI. The method focuses on finding the scattering ansatz systematically and incorporates a pruning technique to achieve the minimum number of participating modes, which guarantees stable practical implementation. Using full-wave analysis of realistic human brain models under a 7 Tesla MRI, we demonstrate more than a twofold improvement in field homogeneity and suppressed local heating, achieving better performance than even the commercial 3 Tesla MRI. The result shows a noninvasive generalization of constant intensity waves in optics, offering a universal methodology applicable to higher Tesla MRI. © 2024, CC BY.

关键词： Magnetic resonance imaging

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Noise Robustness of Data Re-Uploading Quantum Classifiers 4

Noise Robustness of Data Re-Uploading Quantum Classifiers

引用

4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Molina, Daniel E. Hamilton, Kathleen University of Southern California Ming Hsieh Electrical and Computer Engineering Department Los AngelesCA United States Computational Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350343236

We investigate the robustness of quantum neural networks (QNNs) trained as binary classifiers against bit flip and depolarizing noise, two channels that emulate a subset of noise present in noisy intermediate-scale quantum (NISQ) devices. The presence of decoherence and other forms of noise poses significant challenges to classification accuracy. To assess the impact of noise on label extraction, and to evaluate the performance and robustness of our models, we trained three different label assignment methods in the presence of various levels of noise. Each QNN model uses 2 qubits to implement a parameterized model and either the qubits of the QNN, or a set of ancilla qubits are used for label extraction. These three different methods project the state of either a single qubit or multiple qubits onto the computational basis. This generates a probability distribution that is post-processed to assign a class label and a class probability. Each QNN is trained using noisy numerical simulation on a dataset with 2 features. Depolarizing noise channels are interleaved with the parameterized gates of the QNN and we include bit flip error prior to extracting the labels. The noise levels for the depolarizing noise and bit flip error are swept over large ranges of values. We deploy QNNs trained at various noise levels on IBM quantum computers- this introduces an additional source of noise due to SWAP gate noise. We observe that the QNNs trained with simulated noise can have good performance on NISQ hardware. © 2023 IEEE.

关键词： Depolarization

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Approximately Counting and Sampling Hamiltonian Motifs in Sublinear Time

arXiv

引用

arXiv 2025年

作者： Eden, Talya Levi, Reut Ron, Dana Rubinfeld, Ronitt Department of Computer Science Bar Ilan University Israel Efi Arazi School of Computer Science Reichman University Israel School of Electrical and Computer Engineering Tel Aviv University Israel Computer Science and Artificial Intelligence Laboratory MIT United States

Counting small subgraphs, referred to as motifs, in large graphs is a fundamental task in graph analysis, extensively studied across various contexts and computational models. In the sublinear-time regime, the relaxed problem of approximate counting has been explored within two prominent query frameworks: the standard model, which permits degree, neighbor, and pair queries, and the strictly more powerful augmented model, which additionally allows for uniform edge sampling. Currently, in the standard model, (optimal) results have been established only for approximately counting edges, stars, and cliques, all of which have a radius of one. This contrasts sharply with the state of affairs in the augmented model, where algorithmic results (some of which are optimal) are known for any input motif, leading to a disparity which we term the "scope gap" between the two models. In this work, we make significant progress in bridging this gap. Our approach draws inspiration from recent advancements in the augmented model and utilizes a framework centered on counting by uniform sampling, thus allowing us to establish new results in the standard model and simplify on previous results. In particular, our first, and main, contribution is a new algorithm in the standard model for approximately counting any Hamiltonian motif in sublinear time, where the complexity of the algorithm is the sum of two terms. One term equals the complexity of the known algorithms by Assadi, Kapralov, and Khanna (ITCS 2019) and Fichtenberger and Peng (ICALP 2020) in the (strictly stronger) augmented model and the other is an additional, necessary, additive overhead. Our second contribution is a variant of our algorithm that enables nearly uniform sampling of these motifs, a capability previously limited in the standard model to edges and cliques. Our third contribution is to introduce even simpler algorithms for stars and cliques by exploiting their radius-one property. As a result, we simplify all previo

关键词： Stars

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Temperature-Dependent Hydrogen Modulations of Ultra-Scaled a-IGZO Thin Film Transistor Under Gate Bias Stress

IEEE Open Journal of Nanotechnology

引用

IEEE Open Journal of Nanotechnology 2024年 5卷 9-16页

作者： Aslam, Muhammad Chang, Shu-Wei Chuang, Min-Hui Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan701401 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Recently, a-IGZO has advanced toward the next-generation electronics system because of its compatibility with complementary metal oxide semiconductor (CMOS) and back-end-of-line (BOEL) based systems. A systematic electrical characterization of a-IGZO TFT related to reliability issues, such as positive bias temperature stress (PBTS) and negative bias temperature stress (NBTS), would entitle its integration into novel electronics systems. Unexpectedly, PBTS is characterized by the transition of positive Vth shift to negative Vth shift (ΔVth, the positive shift followed by the stress and temperature activated negative shift). This transition is attributed to charge trapping/trap-site generations and hydrogen migration to the active layer. The ΔVth shift mechanism depends on the temperature and voltage stress. On the other hand, a negative ΔVth shift has been observed during the NBTS operation and could be attributed to the hole trapping at the interface of GI/IGZO. An effective suppression of the gate leakage current has also been observed during reliability tests. Simulation results reveal a pronounced potential at the edges of source and drain regions, and considered the origin of hydrogen migration into the IGZO layer. Thermal image results also reveal the strong temperature/potential distribution at the edges of the source/drain regions, indorsing the simulation results. © 2020 IEEE.

关键词： Oxide semiconductors

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Dynamics of self-hybridized exciton–polaritons in 2D halide perovskites

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Light(science & Applications) 2024年第1期13卷 13-22页

作者： Surendra B.Anantharaman Jason Lynch Christopher E.Stevens Christopher Munley Chentao Li Jin Hou Hao Zhang Andrew Torma Thomas Darlington Francis Coen Kevin Li Arka Majumdar PJames Schuck Aditya Mohite Hayk Harutyunyan Joshua R.Hendrickson Deep Jariwala Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA19104USA KBR Inc. BeavercreekOH45431USA Air Force Research Laboratory Sensors DirectorateWright-Patterson Air Force BaseOH45433USA Department of Physics University of WashingtonSeattleWA98195USA Department of Physics Emory UniversityAtlantaGA30322USA Department of Chemical and Biomolecular Engineering Rice UniversityHoustonTX77005USA Department of Materials Science and Nanoengineering Rice UniversityHoustonTX77005USA Applied Physics Program Smalley‐Curl InstituteRice UniversityHoustonTX77005USA Department of Mechanical Engineering Columbia UniversityNew YorkNY10027USA Department of Electrical and Computer Engineering University of WashingtonSeattleWA98195USA

Excitons,bound electron–hole pairs,in two-dimensional hybrid organic inorganic perovskites(2D HOIPs)are capable of forming hybrid light-matter states known as exciton-polaritons(E–Ps)when the excitonic medium is confined in an optical *** the case of 2D HOIPs,they can self-hybridize into E–Ps at specific thicknesses of the HOIP crystals that form a resonant optical cavity with the ***,the fundamental properties of these self-hybridized E–Ps in 2D HOIPs,including their role in ultrafast energy and/or charge transfer at interfaces,remain ***,we demonstrate that>0.5µm thick 2D HOIP crystals on Au substrates are capable of supporting multiple-orders of self-hybridized E–P *** E–Ps have high Q factors(>100)and modulate the optical dispersion for the crystal to enhance sub-gap absorption and *** varying excitation energy and ultrafast measurements,we also confirm energy transfer from higher energy E–Ps to lower energy E–***,we also demonstrate that E–Ps are capable of charge transport and transfer at *** findings provide new insights into charge and energy transfer in E–Ps opening new opportunities towards their manipulation for polaritonic devices.

关键词： charge polar excitation

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GCM-PDA: A Generative Compensation Model for Progressive Difference Attenuation in Spatiotemporal Fusion of Remote Sensing Images

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 34卷 3817-3832页

作者： Kai Ren Weiwei Sun Xiangchao Meng Gang Yang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Department of Geography and Spatial Information Techniques Ningbo University Ningbo China Ningbo Key Laboratory of Remote Sensing and Ecological Security of Coastal Zone Ningbo China Electrical Engineering and Computer Science Ningbo University Ningbo China

High-resolution satellite imagery with dense temporal series is crucial for long-term surface change monitoring. Spatiotemporal fusion seeks to reconstruct remote sensing image sequences with both high spatial and temporal resolutions by leveraging prior information from multiple satellite platforms. However, significant radiometric discrepancies and large spatial resolution variations between images acquired from different satellite sensors, coupled with the limited availability of prior data, present major challenges to accurately reconstructing missing data using existing methods. To address these challenges, this paper introduces GCM-PDA, a novel generative compensation model with progressive difference attenuation for spatiotemporal fusion of remote sensing images. The proposed model integrates multi-scale image decomposition within a progressive fusion framework, enabling the efficient extraction and integration of information across scales. Additionally, GCM-PDA employs domain adaptation techniques to mitigate radiometric inconsistencies between heterogeneous images. Notably, this study pioneers the use of style transformation in spatiotemporal fusion to achieve spatial-spectral compensation, effectively overcoming the constraints of limited prior image information. Experimental results demonstrate that GCM-PDA not only achieves competitive fusion performance but also exhibits strong robustness across diverse conditions.

关键词： Spatiotemporal phenomena MODIS Spatial resolution Remote sensing Landsat Sensors Reflectivity Image reconstruction Data mining Bayes methods

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?-net: Dual supervised medical image segmentation with multi-dimensional self-attention and diversely-connected multi-scale convolution

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NEUROCOMPUTING 2022年 500卷 177-190页

作者： Xu, Zhenghua Liu, Shijie Yuan, Di Wang, Lei Chen, Junyang Lukasiewicz, Thomas Fu, Zhigang Zhang, Rui State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology China Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health Hebei University of Technology China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science University of Oxford United Kingdom Department of Health Management Center 983 Hospital of Joint Logistics Support Force China www.ruizhang.info

Although U-Net and its variants have achieved some great successes in medical image segmentation tasks, their segmentation performances for small objects are still unsatisfactory. Therefore, in this work, a new deep model, co-Net, is proposed to achieve more accurate medical image segmentations. The advancements of co-Net are mainly threefold: First, it incorporates an additional expansive path into U-Net to import an extra supervision signal and obtain a more effective and robust image segmentation by dual supervision. Then, a multi-dimensional self-attention mechanism is further developed to highlight salient features and suppress irrelevant ones consecutively in both spatial and channel dimensions. Finally, to reduce semantic disparity between the feature maps of the contracting and expansive paths, we further propose to integrate diversely-connected multi-scale convolution blocks into the skip connections, where several multi-scale convolutional operations are connected in both series and parallel. Extensive experimental results on three abdominal CT segmentation tasks show that (i) co-Net greatly outperforms the state-of-the-art image segmentation methods in medical image segmentation tasks;(ii) the proposed three advancements are all effective and essential for co-Net to achieve the superior performances;and (iii) the proposed multi-dimensional self-attention (resp., diversely-connected multi scale convolution) is more effective than the state-of-the-art attention mechanisms (resp., multi-scale solutions) for medical image segmentations. The code will be released online after this paper is formally accepted. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Medical image segmentation Dual supervision Multi-dimensional self-attention Diversely-connected multi-scale convolution

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