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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Yang, Xiaolong Deng, Hui Duan, Xuting Xia, Haiying School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China China Telecom Digital City Technology Co. Ltd. Hebei China Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport Beijing100088 China

ISBN: (纸本)9789819711024

With the addition of UAVs, the perception and recognition of ground targets have also advanced significantly. UAV photography has gradually become an integral part of this field. Due to the flexibility and lightness of UAVs, they can freely adjust their height and angle. Moreover, multiple UAVs can perceive and recognize the same target from different directions, which has led to increased attention toward collaborative perception using multiple viewpoints of UAVs. Given the diverse image information captured by UAVs from various angles, heights, and environments, i.e., multi-scale conditions, this paper addresses the challenge of target detection by leveraging the Local Feature Matching with Transformer (LoFTR) approach. The paper also explains the process of coordinating transformations for different images captured by multiple UAVs to ensure data consistency and synergy. Finally, experimental validation using the VisDrone dataset demonstrates that the developed detection method can reliably and efficiently operate on targets at different angles and altitudes. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Object detection

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Data-driven Control of Event-triggered Linear systems 11

Data-driven Control of Event-triggered Linear Systems

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11th IEEE Data Driven Control and Learning systems Conference, DDCLS 2022

作者： Wang, Xin Sun, Jian Wang, Gang State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

The present paper considers the data-driven control of unknown linear time-invariant discrete-time systems under an event-triggering transmission scheme. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional (DLF) approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which offers a way of co-designing the ETS matrix and the controller using pre-collected noisy input-state data. Finally, numerical simulations showcase the efficacy of ETS in reducing data transmissions as well as of the proposed co-design methods. © 2022 IEEE.

关键词： Stability criteria

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Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos

arXiv

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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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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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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 labeled training examples are inadequate while the unlabeled examples are abundant. Due to the scarcity of labeled examples, the performances of the existing SSL methods are often affected by the outliers in the labeled 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 labeled 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 available 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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Directed Acyclic Transformer for Non-Autoregressive Machine Translation 39

Directed Acyclic Transformer for Non-Autoregressive Machine ...

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39th International Conference on Machine Learning, ICML 2022

作者： Huang, Fei Zhou, Hao Liu, Yang Li, Hang Huang, Minlie The CoAI Group Tsinghua University China Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University China ByteDance AI Lab

Non-autoregressive Transformers (NATs) significantly reduce the decoding latency by generating all tokens in parallel. However, such independent predictions prevent NATs from capturing the dependencies between the tokens for generating multiple possible translations. In this paper, we propose Directed Acyclic Transfomer (DA-Transformer), which represents the hidden states in a Directed Acyclic Graph (DAG), where each path of the DAG corresponds to a specific translation. The whole DAG simultaneously captures multiple translations and facilitates fast predictions in a non-autoregressive fashion. Experiments on the raw training data of WMT benchmark show that DA-Transformer substantially outperforms previous NATs by about 3 BLEU on average, which is the first NAT model that achieves competitive results with autoregressive Transformers without relying on knowledge distillation. Copyright Â© 2022 by the author(s)

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