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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingqi Bai Jingkai Zhou Benzhi Wang Weihua Chen Yang Yang Zhen Lei Fan Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Alibaba Group Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Transparent videos with alpha channels play a crucial role in film production, advertising, and augmented reality fields. However, there is currently no available method for producing transparent videos. Traditional methods are time-consuming and labor-intensive, and employing alternative approaches for this task will result in inaccurate transparent regions, constrained motion, and artifacts. To address these challenges, we propose Layer-Animate, the first method capable of generating transparent videos. Our method comprises two stages: in the first stage, transparent images are generated as the base images to provide content and transparency information for the next stage. In the second stage, Inter-Frame Attention is applied to decouple content from motion, enabling the motion module to focus better on action. Layer-Animate is the first method used to generate transparent videos with accurate transparent regions, sufficient motion, and no artifacts, as demonstrated by notable improvements in qualitative and quantitative metrics.

关键词： Accuracy Production Signal processing Acoustic measurements Robustness Acoustics Speech processing Advertising Augmented reality Videos

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Trustworthy Bayesian Perceptrons

Trustworthy Bayesian Perceptrons

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International Conference on Information Fusion

作者： Markus Walker Hayk Amirkhanian Marco F. Huber Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Department Cyber Cognitive Intelligence (CCI) Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Bayesian Neural Networks (BNNs) offer a sophisticated framework for extending classical neural network point estimates to encompass predictive distributions. Despite the high potential of BNNs, established BNN training methods such as Variational Inference (VI) and Markov Chain Monte Carlo (MCMC) grapple with issues such as scalability and hyperparameter dependence. In addressing these issues, our research focuses on the fundamental elements of BNNs, in particular perceptrons and their predictive capabilities. We introduce a new perspective on the closed-form solution for backward-pass computation for the Bayesian perceptron and prove that the state-of-the-art solution is equivalent to statistical linearization. To assess the efficacy of Bayesian perceptrons and provide insights into their performance in distinct input space regions, a novel methodology utilizing k-d trees as a space partitioning method is introduced to evaluate prediction quality within specific input space regions.

关键词： Training Systematics Monte Carlo methods Scalability Neural networks Estimation Predictive models Nonhomogeneous media Bayes methods Testing

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3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation

3D Face Reconstruction with the Geometric Guidance of Facial...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zidu Wang Xiangyu Zhu Tianshuo Zhang Baiqin Wang Zhen Lei State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D Morphable Models (3DMMs) provide promising 3D face reconstructions in various applications. However, existing methods struggle to reconstruct faces with extreme expressions due to deficiencies in supervisory signals, such as sparse or inaccurate landmarks. Segmentation information contains effective geometric contexts for face reconstruction. Certain attempts intuitively depend on differentiable renderers to compare the rendered silhouettes of reconstruction with segmentation, which is prone to issues like local optima and gradient instability. In this paper, we fully utilize the facial part segmentation geometry by introducing Part Re-projection Distance Loss (PRDL). Specifically, PRDL transforms facial part segmentation into 2D points and re-projects the reconstruction onto the image plane. Subsequently, by introducing grid anchors and computing different statistical distances from these anchors to the point sets, PRDL establishes geometry descriptors to optimize the distribution of the point sets for face reconstruction. PRDL exhibits a clear gradient compared to the renderer-based methods and presents state-of-the-art reconstruction performance in extensive quantitative and qualitative experiments. Our project is available at https://***/wang-zidu/3DDFA-V3.

关键词： Geometry Image segmentation Solid modeling Computer vision Three-dimensional displays Annotations Face recognition

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A new approach of modeling the ultra-short channel ballistic GAA MOSFETs 2

A new approach of modeling the ultra-short channel ballistic...

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2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022

作者： Cheng, He Zhang, Chao Xie, Chuang Liu, Tiefneg Yang, Zhijia Zhang, Zhipeng Key Laboratory of Networked Control Systems Chinese Academy of Sciences Liaoning Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110169 China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

Compact models are useful to avoid spending a numerous amount of time in numerical simulations which can accurately evaluate the ultra-short channel device properties. One numerical compact model proposed here is describing the drain current in ballistic mode by using one unique smoothing function representing the transmission coefficients for all operating regions. The model is addressing the compact model for the subthreshold region basing on our previous study, in which the DIBL and the SDT effects are taken into a consideration. In this paper, a method of establishing the smoothing function is introduced for expressing the important parameters in the model overall operating regions. Compared with the TCAD NEGF simulation, this proposed numerical compact model demonstrates a good agreement. © 2022 IEEE.

关键词： Ballistics

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UAV Path Planning Based on Simultaneous Optimization of Monitoring Frequency and Security

UAV Path Planning Based on Simultaneous Optimization of Moni...

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Chinese Control and Decision Conference, CCDC

作者： Yifei Shu Yang Chen Mian Hu Huaiyu Wu Xingang Zhao Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang

ISBN: (纸本)9781665478977

UAVs have great potential when applied to persistent monitoring, but there are still problems such as difficulty in ensuring the monitoring frequency and easy leakage of monitoring path information. Therefore, it is necessary to increase the UAV monitoring frequency of targets and the randomness of monitoring paths as much as possible on the premise of covering all monitoring tasks. In response to the above problems, this paper studies the UAV path planning problem of simultaneous optimization of monitoring frequency and path security. Through the evaluation of monitoring frequency and path security based on monitoring overdue time and path entropy, a mathematical model for UAV path planning is established. An improved ant colony algorithm based on the monitoring overdue time (Overdue-aware Ant Colony Optimization, OACO) is designed, and finally the UAV flight trajectory with high monitoring frequency and the high monitoring path security is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node to be accessed, improve the security of the UAV monitoring path, which is of great significance for enhancing monitoring security and preventing intrusion.

关键词： Time-frequency analysis Simulation Mathematical models Entropy Trajectory Safety Security

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A Robust Surveillance System for AGV Using Forward-Downward Visual Measurements

A Robust Surveillance System for AGV Using Forward-Downward ...

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第35届中国控制与决策会议

作者： Sichao Zhang Wei Liang Xudong Yuan Haibo An Meng Zheng Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Science and Technology on Information Systems Engineering Laboratory the 28th Research Institute of CETC

Automatic Guided Vehicle(AGV) has been widely used in the warehouse for transporting the bulky and heavy ***,the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to,e.g.,cyber attacks,unexpected blocks of the wireless *** order to ensure AGV running safely,this paper presents a visual surveillance system by making full use of the measurements from the forward and downward ***,the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image ***,the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference *** from that,the AGV poses from the downward camera could correct scale and poses estimated the forward *** proposed method has been extensively performed on the developed *** results proves the effectiveness in using the complementary forwarddownward visual measurements for AGV security surveillance.

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A distributed integrated energy trading solution

A distributed integrated energy trading solution

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2021 International Conference on Computer, Blockchain and Financial Development, CBFD 2021

作者： Dong, Jingya Song, Chunhe Zheng, Zeyu Zhang, Tao Institutes for Robotics and Intelligent Manufacturing University of Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Nationality Department of Digital Factory Shenyang China University of Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (纸本)9781665412278

Blockchain technology can bring profound changes to the energy industry and is now becoming a new driving force for the rapid development of this sector. In order to build a secure, reliable, and stable energy service system, it is necessary to study the framework and interaction mechanism of the energy trading system. This paper introduces a distributed integrated energy trading solution, proposes a new overall architecture, and analyses the blockchain model. This paper presents an algorithm to calculate the score directly in the client. Through this algorithm, users can choose service providers, get rid of cloud control, and realize users' free choice of electricity purchase and interaction between users through the smart contract. The test results show that the system has high performance and efficiency, can meet the needs of integrated energy transactions, and provides a solution for the application of blockchain in energy trading. © 2021 IEEE.

关键词： Smart contract

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Development of a Method for Generating Extremely High Reference Motion Speeds of Mechatronic Objects Moving Alone or in a Group

Development of a Method for Generating Extremely High Refere...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Anton Gubankov Igor Gornostaev Robotics Laboratory Institute of Automation and Control Processes FEB RAS Laboratory of Marine Unmanned Aviation and Marine Aviation Systems Admiral G.I. Nevelskoy Maritime State University Vladivostok Russia Laboratory of Robotic Systems M.D. Ageev Institute of Marine Technology Problems FEB RAS

The paper presents the results of developing a method for generating reference control signals that describe the desired law of movement of various mechatronic objects (unmanned aerial vehicles, mobile land and underwater robots, etc.) along smooth trajectories specified by parametric splines. In order to maintain high dynamic accuracy of control of these mechatronic objects, including when solving group control problems, it is proposed to adjust their reference movement speed, taking into account the specified restrictions on the developed accelerations. Taking into account these restrictions is necessary to reduce the reference speed in sections of the trajectory with increased curvature. To implement the proposed approach, a method was developed and a system was synthesized, the performance of which was confirmed by the results of numerical simulation. The presented system is designed to operate in conditions where the reference trajectory of a mechatronic object is unknown in advance and is formed (or adjusted) directly during the mission without the possibility of preliminary calculations. Also, this system does not require complex calculations and can be implemented on the basis of low-power on-board computers.

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Informative Data Mining for One-shot Cross-Domain Semantic Segmentation

Informative Data Mining for One-shot Cross-Domain Semantic S...

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International Conference on Computer Vision (ICCV)

作者： Yuxi Wang Jian Liang Jun Xiao Shuqi Mei Yuran Yang Zhaoxiang Zhang HKISI-CAS Centre for Artificial Intelligence and Robotics Chinese Academy of Sciences Institute of Automation University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Tencent

Contemporary domain adaptation offers a practical solution for achieving cross-domain transfer of semantic segmentation between labelled source data and unlabeled target data. These solutions have gained significant popularity; however, they require the model to be retrained when the test environment changes. This can result in unbearable costs in certain applications due to the time-consuming training process and concerns regarding data privacy. One-shot domain adaptation methods attempt to overcome these challenges by transferring the pre-trained source model to the target domain using only one target data. Despite this, the referring style transfer module still faces issues with computation cost and over-fitting problems. To address this problem, we propose a novel framework called Informative Data Mining (IDM) that enables efficient one-shot domain adaptation for semantic segmentation. Specifically, IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. We then perform a model adaptation method using these selected samples, which includes patch-wise mixing and prototype-based information maximization to update the model. This approach effectively enhances adaptation and mitigates the overfitting problem. In general, we provide empirical evidence of the effectiveness and efficiency of IDM. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7%/55.4% on the GTA5/SYNTHIA to Cityscapes adaptation tasks, respectively. The code will be released at https://***/yxiwang/IDM.

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Decentralized Reinforcement Learning for Multiple Robotic Fish in Cooperative Pursuit Task

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Feng, Yukai Wu, Zhengxing Wang, Jian Li, Sijie Huang, Yupei Yu, Junzhi Tan, Min Institute of Automation Chinese Academy of Sciences Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China

The control of multi-robot systems, particularly in the pursuit-evasion (PE) with multiple robots, has gained significant attention in both academic and non-academic settings. However, the collaborative operation of multi-robotic fish systems encounters substantial challenges due to the complex underwater environment and unique movement mode. In this paper, we propose a multi-agent reinforcement learning (MARL) approach to develop a viable strategy for underwater cooperative pursuit. Initially, considering the hydrodynamic model and motion characteristics of robotic fish, we construct a specific simulation environment with multiple fish-like agents, which provides a highly realistic state transition model. Next, we develop a MARL-based strategy learning framework that incorporates appropriate reward functions and agent actions for policy learning. Finally, a series of comprehensive simulations and practical experiments are conducted to validate the effectiveness of the proposed method and confirm its successful application in underwater pursuit scenarios. These findings offer valuable insights for further research in underwater multiple robot systems. © 2016 IEEE.

关键词： Chatbots

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