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arXiv 2023年

作者： Wang, Yikai Dong, Yinpeng Sun, Fuchun Yang, Xiao State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China RealAI China

This work focuses on the 3D reconstruction of non-rigid objects based on monocular RGB video sequences. Concretely, we aim at building high-fidelity models for generic object categories and casually captured scenes. To this end, we do not assume known root poses of objects, and do not utilize category-specific templates or dense pose priors. The key idea of our method, Root Pose Decomposition (RPD), is to maintain a per-frame root pose transformation, meanwhile building a dense field with local transformations to rectify the root pose. The optimization of local transformations is performed by point registration to the canonical space. We also adapt RPD to multi-object scenarios with object occlusions and individual differences. As a result, RPD allows non-rigid 3D reconstruction for complicated scenarios containing objects with large deformations, complex motion patterns, occlusions, and scale diversities of different individuals. Such a pipeline potentially scales to diverse sets of objects in the wild. We experimentally show that RPD surpasses state-of-the-art methods on the challenging DAVIS, OVIS, and AMA datasets. We provide video results in https://***. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Distributed state and Fault Fusion Filtering for Nonlinear systems Subject to Quantization Effects 8

Distributed State and Fault Fusion Filtering for Nonlinear S...

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8th International Conference on Control Science and systems Engineering, ICCSSE 2022

作者： Hu, Zhibin Hu, Jun Du, Junhua Zhao, Yuna Harbin University of Science and Technology Department of Mathematics Heilongjiang Prov. Key Lab. of Optimization Control and Intelligent Analysis for Complex Systems Harbin China School of Automation Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781665484138

In this paper, the distributed state and fault fusion filtering problem is discussed for a class of multi-sensor networked systems (MSNSs) with randomly occurring quantized measurements (ROQMs). A series of random variables obeying the Bernoulli distribution are adopted to depict the phenomenon of the ROQMs and the fault signal is assumed to appear with an unknown constant type. The aim of this paper is to provide the state and fault fusion filtering algorithm by using the inverse covariance intersection fusion method. Moreover, a proper filter gain is designed by minimizing the local upper bound of filtering error covariance. Finally, the effectiveness of the presented fusion method is verified by using an illustrative example. © 2022 IEEE.

关键词： Inverse problems

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An Adaptive Event-triggered Nonlinear Filtering for Power systems Subject to Hybrid Measurements and Multiple Cyber-Attacks 8

An Adaptive Event-triggered Nonlinear Filtering for Power Sy...

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8th International Conference on Control Science and systems Engineering, ICCSSE 2022

作者： Huang, Jinpeng Hu, Jun Li, Jiaxing Cao, Zhipeng Harbin University of Science and Technology Department of Mathematics Harbin150080 China Harbin University of Science and Technology Heilongjiang Prov. Key Lab. of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China

ISBN: (纸本)9781665484138

In this paper, an adaptive event-triggered filtering problem is discussed for power systems subject to multiple cyberattacks and hybrid measurements. A model describing the multiple cyber-attacks is constructed, which includes the denial of service attack and replay attack. In order to decrease the data transmission frequency, according to the practical requirements, an adaptive event-triggered strategy is adopted to schedule the node transmission. To be specific, the upper bound of the estimation error covariance is firstly obtained by solving the difference equations and employing the time-stamp method. Next, the desired estimator gain matrix is designed by minimizing the corresponding upper bound. Finally, an illustrative example is used to demonstrate the effectiveness of the proposed estimation algorithm. © 2022 IEEE.

关键词： Denial-of-service attack

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Low-noise Microwave Signal Generation by Heterodyning Two Narrow-linewidth Self-injection Locked Lasers

Low-noise Microwave Signal Generation by Heterodyning Two Na...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Fan, Yihao Siyu, E. Guo, Yuyao Li, Xinhang Lu, Liangjun Wang, Shuxiao Cai, Yan Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Shanghai Industrial μTechnology Research Institute Shanghai201800 China

We realize a hybrid integrated self-injection locking laser (SIL) with an ultra-narrow intrinsic linewidth of 5 Hz. With heterodyne synthesis using a pair of SILs, a microwave signal with a 4-kHz linewidth is achieved... 详细信息

ISBN: (纸本)9781957171395

关键词： Linewidth

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Low-noise Microwave Signal Generation by Heterodyning Two Narrow-linewidth Self-injection Locked Lasers

Low-noise Microwave Signal Generation by Heterodyning Two Na...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Linewidth

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Variational Bayesian Learning based Joint Localization and Channel Parameter Estimation in Wireless Sensor Networks with Distance-dependent Noise

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IEEE Transactions on Vehicular technology 2024年第5期74卷 6877-6891页

作者： Li, Yunfei Luo, Yiting Tan, Weiqiang Li, Chunguo Ma, Shaodan Yang, Guanghua Anhui Polytechnic University Department of Electrical Engineering Wuhu China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China Southeast University School of Information Science and Engineering Nanjing China Purple Mountain Laboratories Nanjing China University of Macau State Key Lab. of Internet of Things for Smart City and the Dept. of Elec. and Computer Engineering Taipa China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China

This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distancedependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoretical analyses and experimental results. The distance-dependent noise introduces a complex noise model with unknown noise power and PLE, resulting in an exceptionally challenging nonconvex and nonlinear optimization problem. In this study, we address a joint localization and path loss exponent estimation problem encompassing distance-dependent noise, unknown parameters, and uncertainties in sensor node locations. To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution. Furthermore, the proposed joint learning algorithm provides an iterative closed-form solution and exhibits superior performance in terms of computational complexity compared to existing algorithms. Computer simulation results demonstrate that the proposed algorithm approaches the performance of the Bayesian Cramer-Rao bound (BCRB), achieves localization performance comparable to the (maximum likelihood-Gaussian message passing) ML-GMP algorithm in some cases, and outperforms the other comparison algorithm in all cases. © 1967-2012 IEEE.

关键词： Iterative methods

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Online Learning of Linear Quadratic Gaussian Controllers from Noisy Data

Online Learning of Linear Quadratic Gaussian Controllers fro...

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International Conference on Control and Automation (ICCA)

作者： Linqi Wang Wenjie Liu Yifei Li Jian Sun Gang Wang State Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper addresses the joint state estimation and online control problems of unknown linear time-invariant systems subject to process and measurement noises. The proposal is to design a finite-horizon linear quadratic Gaussian (LQG) controller from noisy data. To achieve this, relaxed data-based semi-definite programs (SDPs) are constructed, upon solving which, a robust finite-horizon linear quadratic regulator (LQR) and a robust Kalman filter are developed, and consequently, a robust data-driven finite-horizon LQG controller is designed. It is shown that the proposed data-driven finite-horizon LQG controller ensures robust global exponential stability (RGES) of the observer and the input-to-state stability (ISS) of the closed-loop system under standard conditions. Finally, a numerical example is provided to demonstrate its effectiveness.

关键词： Regulators Stability Noise Process control Observers Noise measurement Kalman filters

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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Laplacian Welsch Regularization for Robust Semisupervised Learning

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IEEE Transactions on Cybernetics 2022年第1期52卷 164-177页

作者： Ke, Jingchen Gong, Chen Liu, Tongliang Zhao, Lin Yang, Jian Tao, Dacheng PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China UBTECH Sydney Artificial Intelligence Centre University of Sydney DarlingtonNSW Australia

Semisupervised learning (SSL) has been widely used in numerous practical applications where the lab.led training examples are inadequate while the unlab.led examples are abundant. Due to the scarcity of lab.led examples, the performances of the existing SSL methods are often affected by the outliers in the lab.led data, leading to the imperfect trained classifier. To enhance the robustness of SSL methods to the outliers, this article proposes a novel SSL algorithm called Laplacian Welsch regularization (LapWR). Specifically, apart from the conventional Laplacian regularizer, we also introduce a bounded, smooth, and nonconvex Welsch loss which can suppress the adverse effect brought by the lab.led outliers. To handle the model nonconvexity caused by the Welsch loss, an iterative half-quadratic (HQ) optimization algorithm is adopted in which each subproblem has an ideal closed-form solution. To handle the large datasets, we further propose an accelerated model by utilizing the Nyström method to reduce the computational complexity of LapWR. Theoretically, the generalization bound of LapWR is derived based on analyzing its Rademacher complexity, which suggests that our proposed algorithm is guaranteed to obtain satisfactory performance. By comparing LapWR with the existing representative SSL algorithms on various benchmark and real-world datasets, we experimentally found that LapWR performs robustly to outliers and is able to consistently achieve the top-level results. © 2013 IEEE.

关键词： Large dataset

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UNIVERSAL VISION-LANGUAGE DENSE RETRIEVAL: LEARNING A UNIFIED REPRESENTATION SPACE FOR MULTI-MODAL RETRIEVAL 11

UNIVERSAL VISION-LANGUAGE DENSE RETRIEVAL: LEARNING A UNIFIE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Liu, Zhenghao Xiong, Chenyan Lv, Yuanhuiyi Liu, Zhiyuan Yu, Ge Department of Computer Science and Technology Northeastern University Shenyang China Microsoft Research Redmond United States Department of Computer Science and Technology Tsinghua University Beijing China Institute for Artificial Intelligence Tsinghua University Beijing China State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China

This paper presents Universal Vision-Language Dense Retrieval (UniVL-DR), which builds a unified model for multi-modal retrieval. UniVL-DR encodes queries and multi-modality resources in an embedding space for searching candidates from different modalities. To learn a unified embedding space for multi-modal retrieval, UniVL-DR proposes two techniques: 1) Universal embedding optimization strategy, which contrastively optimizes the embedding space using the modality-balanced hard negatives;2) Image verbalization method, which bridges the modality gap between images and texts in the raw data space. UniVL-DR achieves the state-of-the-art on the multi-modal open-domain question answering benchmark, WebQA, and outperforms all retrieval models on the two subtasks, text-text retrieval and text-image retrieval. It demonstrates that universal multi-modal search is feasible to replace the divide-and-conquer pipeline with a united model and also benefits single/cross modality tasks. All source codes of this work are availab.e at https://***/OpenMatch/UniVL-DR. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Embeddings

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