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arXiv 2024年

作者： Zhang, Dingwen Li, Hao He, Diqi Liu, Nian Cheng, Lechao Wang, Jingdong Han, Junwei The Brain and Artificial Intelligence Lab. Northwestern Polytechnical University Shaanxi Xi’an710000 China The Inception Institute of Artificial Intelligence United Kingdom The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Department of Computer Vision Baidu Inc. China

In recent times, following the paradigm of DETR (DEtection TRansformer), query-based end-to-end instance segmentation (QEIS) methods have exhibited superior performance compared to CNN-based models, particularly when trained on large-scale datasets. Nevertheless, the effectiveness of these QEIS methods diminishes significantly when confronted with limited training data. This limitation arises from their reliance on substantial data volumes to effectively train the pivotal queries/kernels that are essential for acquiring localization and shape priors. To address this problem, we propose a novel method for unsupervised pre-training in low-data regimes. Inspired by the recently successful prompting technique, we introduce a new method, Unsupervised Pre-training with Language-vision Prompts (UPLVP), which improves QEIS models’ instance segmentation by bringing language-vision prompts to queries/kernels. Our method consists of three parts: (1) Masks Proposal: Utilizes language-vision models to generate pseudo masks based on unlabeled images. (2) Prompt-Kernel Matching: Converts pseudo masks into prompts and injects the best-matched localization and shape features to their corresponding kernels. (3) Kernel Supervision: Formulates supervision for pre-training at the kernel level to ensure robust learning. With the help of our pre-training method, QEIS models can converge faster and perform better than CNN-based models in low-data regimes. Experimental evaluations conducted on MS COCO, Cityscapes, and CTW1500 datasets indicate that the QEIS models’ performance can be significantly improved when pre-trained with our method. Code will be available at: https://***/lifuguan/UPLVP. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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2mm Diameter Continuum Robot Tools for Suturing in Open Spina Bifida Repair

2mm Diameter Continuum Robot Tools for Suturing in Open Spin...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Arion Law Nillan Nimal Paul H. Kang Radian Gondokaryono James Drake Tim Van Mieghem Thomas Looi Department of Mechanical and Industrial Engineering University of Toronto Canada Wilfred and Joyce Posluns Centre for Image Guided Innovation and Therapeutic Intervention Hospital for Sick Children Toronto Canada Institute of Biomedical Engineering University of Toronto Canada Department of Computer Science and the Medical Computer Vision and Robotics Lab University of Toronto Canada Mount Sinai Hospital Toronto Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Open Spina Bifida (OSB) is a congenital neural tube defect where a major component of the procedure to repair the defect involves the closure of a lesion wound through suturing. For a minimally invasive approach, tools entering the uterus to access the fetus should be as thin as possible to minimize maternal risk. This work presents the design of a 3 degrees-of-freedom, 2mm diameter tool wrist with a bending range of motion from 0° to 90°. This wrist is capable of generating up to 2N of force measured from the end of the wrist and achieving a bending curvature of 107m -1 (9.35mm bending radius). A pseudo-rigid body kinematic model has been implemented for the control of this tool with a protocol for singularity mitigation and avoidance. Timed teleoperation studies explicitly demonstrate that the tool is able to reliably execute suturing with a fastest achieved time of under 3 minutes for a simple interrupted suturing technique.

关键词： Wrist Protocols Prevention and mitigation Uterus Force Bending Maintenance engineering Wounds Reliability engineering Motion control

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Removing Scattered Light in Biomedical Images via an Unsupervised Deep Neural Network

Removing Scattered Light in Biomedical Images via an Unsuper...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Yuanhao Gong Meng Xu Yawei Li Michele Magno Faculty of Electronics and Information Engineering Shenzhen University China School of Computer Science Queen Mary University of London U.K. Computer Vision Lab ETH Zurich Switzerland Center for Project-based Learning ETH Zurich Switzerland

Scattered light is unavoidable during biomedical imaging, leading to downgraded images. Removing such scattered light, however, is challenging, because the imaging objects are complex and the light path is complicated. In this paper, we propose to use deep learning methods to computationally remove such scattered light. Inspired by the dehazing methods for natural images, we first developed a novel mathematical model for biomedical images. Then, we developed a deep neural network method to solve this model. Our network simultaneously estimates the clear image and the scattered light. Several experiments are conducted to confirm the network’s effectiveness and efficiency.

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Leveraging PID gain selection towards adaptive backstepping control for a class of second-order systems

arXiv

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arXiv 2021年

作者： Kourani, Ahmad Daher, Naseem Vision and Robotics Lab Department of Mechanical Engineering American University of Beirut Beirut Lebanon Vision and Robotics Lab Department of Electrical & Computer Engineering

—In this work, we establish a convenient similarity between an adaptive backstepping control law and a standard proportional-integral-derivative (PID) controller for a class of second-order systems. The extracted similarity provides a deeper understanding of the adaptive backstepping design from a performance perspective via an intuitive method to select its otherwise abstract controller gains, on top of its traditional stability perspective. Such a similarity analysis opens the door for researchers to use well-established PID tuning methods to predict the performance of Lyapunov stability-based controllers. At the same time, the obtained formulation reveals how the corresponding PID control law can be linked to Lyapunov stability theory. The proposed scheme is applied to a quadrotor unmanned aerial vehicle (UAV) carrying a payload with the presence of a wind gust as an external disturbance, in simulation and experimentally. Copyright © 2021, The Authors. All rights reserved.

关键词： Proportional control systems

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A tethered quadrotor UAV-buoy system for marine locomotion

arXiv

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arXiv 2021年

Unmanned aerial vehicles (UAVs) are finding their way into offshore applications. In this work, we postulate an original system that entails a marine locomotive quadrotor UAV that manipulates the velocity of a floating buoy by means of a cable. By leveraging the advantages of UAVs relative to high speed, maneuverability, ease of deployment, and wide field of vision, the proposed UAV-buoy system paves the way in front of a variety of novel applications. The dynamic model that couples the buoy, UAV, cable, and water environment is presented using the Euler-Lagrange method. A stable control system design is proposed to manipulate the forward-surge speed of the buoy under two constraints: maintaining the cable in a taut state, and keeping the buoy in contact with the water surface. Polar coordinates are used in the controller design process to attain correlated effects on the tracking performance, whereby each control channel independently affects one control parameter. This results in improved performance over traditional Cartesian-based velocity controllers, as demonstrated via numerical simulations in wave-free and wavy seas. Copyright © 2021, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Test-Time Training-Free Domain Adaptation 48

Test-Time Training-Free Domain Adaptation

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Feng, Yongxiang He, Weihua You, Kaichao Liu, Bing Zhang, Ziyang Wang, Yaoyuan Li, Minglei Lou, Yihang Li, Jiawei Li, Guoqi Liao, Jianxing Huawei Technologies Co. Ltd. Advanced Computing and Storage Lab China Peking University School of Electronics Engineering and Computer Science China Huawei Technologies Co. Ltd. Language & Speech Innovation Lab China Huawei Technologies Co. Ltd. GoTen AI Lab Department of Intelligent Vision China Huawei Technologies Co. Ltd. Department of Production Automation Development China Chinese Academy of Sciences Institute of Automation China

ISBN: (纸本)9781728163277

Deploying deep learning models to new environments is very challenging. Domain adaptation (DA) is a promising paradigm to solve the problem by collecting and adapting to unlabeled data in new environments. Though research efforts have led to steady performance improvement over the past decade, DA algorithms are still hard to deploy, as training on unlabeled new data makes tuning difficult and not feasible for inference-only devices. To make DA practical, in this paper we study a new problem named Test-time Training-Free Domain Adaptation (TTDA), where trained models must adapt to a single input (mimicking the test-time scenario) without training. By exploiting spatial activation that was previously overlooked and simply averaged out, we propose a simple method based on Feature Statistics Transformation (FST) on-the-fly for each test example. The proposed algorithm is tested in the TTDA setting on two standard DA benchmarks. Surprisingly, it surpasses or performs on par with state-of-the-art DA methods, even though they require additional training. We envision that this training-free paradigm has the potential to bring DA to embedded devices and would be of interest to audience of community. © 2023 IEEE.

关键词： Deep learning

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Ring partition-based fingerprint indexing algorithm 1

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2nd International Conference on Machine Intelligence and Signal Processing, MISP 2019

作者： Gogoi, Manas Jyoti Trivedi, Amit Kumar Thounaojam, Dalton Meitei Bhattarchajee, Aniruddha Debnath, Rahul Borah, Kaushik Computer Vision Lab Department of Computer Science and Engineering National Institute of Technology Silchar Assam788010 India

ISBN: (数字)9789811513664

ISBN: (纸本)9789811513657

The shear number of fingerprints in a modern database makes exhaustive search, a computationally an expensive process. It is in this context a new indexing mechanism is proposed to speed up the process of identification of fingerprints. In this model, concentric circles are made around the core of a fingerprint, and by grouping the fingerprints according to the number of minutiae points in each of these rings, we can select the best prospective fingerprints for a particular query fingerprint, thus greatly reducing the number of fingerprints in which exhaustive search is to be employed and increasing the speed of Automated Fingerprint Identification Systems (AFIS). The proposed model was tested on different datasets of FVC2000 database, and the results show that the model achieves high CIP with a low penetration rate and our model was able to significantly speed up the process. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Query processing

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Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition

arXiv

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arXiv 2022年

作者： Truong, Thanh-Dat Duong, Chi Nhan Le, Ngan Savvides, Marios Luu, Khoa The Computer Vision and Image Understanding Lab University of Arkansas United States The Department of Computer Science and Software Engineering Concordia University Canada Carnegie Mellon University United States

In this work, we investigate the problem of face reconstruction given a facial feature representation extracted from a blackbox face recognition engine. Indeed, it is a very challenging problem in practice due to the limitations of abstracted information from the engine. We, therefore, introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) to synthesize the faces of a subject given his/her extracted face recognition features. Given any unconstrained unseen facial features of a subject, the DAB-GAN can reconstruct his/her facial images in high definition. The DAB-GAN method includes a novel attention-based generative structure with the newly defined Bijective Metrics Learning approach. The framework starts by introducing a bijective metric so that the distance measurement and metric learning process can be directly adopted in the image domain for an image reconstruction task. The information from the blackbox face recognition engine will be optimally exploited using the global distillation process. Then an attention-based generator is presented for a highly robust generator to synthesize realistic faces with ID preservation. We have evaluated our method on the challenging face recognition databases, i.e., CelebA, LFW, CFP-FP, CP-LFW, AgeDB, CA-LFW, and consistently achieved state-of-the-art results. The advancement of DAB-GAN is also proven in both image realism and ID preservation properties. Copyright © 2022, The Authors. All rights reserved.

关键词： Face recognition

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Ultra-Compact Magnetoelectric Sensors for Femto-Tesla Vlf Signal Reception

SSRN

引用

SSRN 2024年

作者： Dong, Cunzheng Sun, Changxing Chen, Lei He, Yifan Liu, Yisi Luo, Bin Sun, Nian-Xiang Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Key Lab of Computer Vision and Intelligent Information System Chongqing University of Arts and Sciences Chongqing402160 China

Very low-frequency (VLF) electromagnetic waves can penetrate dense conductive media, such as earth and saltwater, with minimal attenuation, allowing for long-distance signal transmission via ionospheric reflection. These characteristics make VLF waves ideal for applications in submarine navigation, subterranean mapping, underground communication, and ionospheric remote sensing. Conventional VLF signal reception has relied on magnetic loop antennas due to their low noise performance;however, their large size and reduced sensitivity due to low quality factors (Q) limit their use in portable and compact applications, particularly in underwater and underground environments. To address these challenges, we propose an ultra-compact room-temperature extremely sensitive femto-tesla magnetic sensor based on a strain-mediated high-Q Metglas/Quartz magnetoelectric (ME) resonator operating at its electromechanical resonance (EMR) at 24.55 kHz for VLF signal reception. The Metglas/Quartz ME sensor demonstrates a sensitivity and magnetic noise performance enhancement by an order of magnitude compared to conventional Metglas/PZT ME sensors, achieving an ultra-low equivalent magnetic noise level of 5 fT/Hz1/2, owing to high magnetic permeability and magnetostriction of Metglas and the high-quality factor of Quartz at EMR. Moreover, the Metglas/Quartz ME VLF receiver exhibits overwhelming near-field and far-field VLF signal reception capability, realizing a successful reception of VLF signal 400 km away from the NAA VLF Transmitter Cutler. The demonstrated ultra-compact high-Q Metglas/Quartz ME sensor capable of femto-tesla VLF signal reception shows significant improvements in magnetic sensing capability, size, power consumption, and cost compared to traditional magnetic loop antennas, making it a promising solution for portable VLF signal reception in challenging environments. © 2024, The Authors. All rights reserved.

关键词： Loop antennas

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