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arXiv 2021年

作者： Zong, Zhuofan Li, Kunchang Song, Guanglu Wang, Yali Qiao, Yu Leng, Biao Liu, Yu School of Computer Science and Engineering Beihang University China SenseTime Research China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Shanghai AI Laboratory China

vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bring a huge computation burden, because of the exhausting token-to-token comparison. The previous works focus on dropping insignificant tokens to reduce the computational cost of ViTs. But when the dropping ratio increases, this hard manner will inevitably discard the vital tokens, which limits its efficiency. To solve the issue, we propose a generic self-slimmed learning approach for vanilla ViTs, namely SiT. Specifically, we first design a novel Token Slimming Module (TSM), which can boost the inference efficiency of ViTs by dynamic token aggregation. As a general method of token hard dropping, our TSM softly integrates redundant tokens into fewer informative ones. It can dynamically zoom visual attention without cutting off discriminative token relations in the images, even with a high slimming ratio. Furthermore, we introduce a concise Feature Recalibration Distillation (FRD) framework, wherein we design a reverse version of TSM (RTSM) to recalibrate the unstructured token in a flexible auto-encoder manner. Due to the similar structure between teacher and student, our FRD can effectively leverage structure knowledge for better convergence. Finally, we conduct extensive experiments to evaluate our SiT. It demonstrates that our method can speed up ViTs by 1.7× with negligible accuracy drop, and even speed up ViTs by 3.6× while maintaining 97% of their performance. Surprisingly, by simply arming LV-ViT with our SiT, we achieve new state-of-the-art performance on ImageNet. Code is available at https://***/Sense-X/SiT. © 2021, CC BY.

关键词： Distillation

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Bebop 2 Quadrotor as a Platform for research and Education in robotics and Control Engineering

Bebop 2 Quadrotor as a Platform for Research and Education i...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Wojciech Giernacki Piotr Kozierski Jacek Michalski Marek Retinger Rafal Madonski Pascual Campoy Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering of Poznan University of Technology Poznań Poland Institute of Computing Science (Faculty of Computing and Telecommunications) and with the Institute of Robotics and Machine Intelligence (Faculty of Control Robotics and Electrical Engineering) Poznan University of Technology Poznań Poland International Energy College Energy Electricity Research Center Jinan University Zhuhai China Computer Vision and Aerial Robotics (CVAR) Centre for Automation and Robotics Technical University of Madrid (UPM) Madrid Spain

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

In conducting research and teaching in fields related to unmanned aerial vehicles (UAVs), it is particularly important to select a universal, safe, open research platform and tools for rapid prototyping. Ready-to-use, low-cost micro-class UAVs such as Bebop 2 are successfully used in that regard. This article presents how to use the potential of this flying robot with Robot Operating System (ROS). The most important software solutions for the developed experimental testbed FlyBebop are characterized here. Their capabilities in research and education are exemplified using three distinct cases: 1) research results on the method of optimal, in-flight, iterative self-tuning of UAV position controller parameters (based only on current measurements), 2) the use of the reinforcement learning method in the autonomous landing of a single drone on a moving vehicle, 3) planning the movement of UAVs for autonomous video recording along the planned path in the arrangement: "cameraman drone" and "lighting technician drones".

关键词： Drones Robots Education Cameras Sensors Tools

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OTE: Optimal Trustworthy EdgeAI solutions for smart cities

OTE: Optimal Trustworthy EdgeAI solutions for smart cities

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Vasileios Mygdalis Lorenzo Carnevale Jose Ramiro Martí nez-De-Dios Dmitriy Shutin Giovanni Aiello Massimo Villari Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mathematical and Computer Science Physics and Hearth Sciences University of Messina Messina Italy Gruppo Nazionale per il Calcolo Scientifico (GNCS) Istituto Nazionale di Alta Matematica (INdAM) &#x201C F. Severi&#x201D Rome Italy Robotics Vision and Control Group University of Seville Seville Spain Institute of Communications and Navigation German Aerospace Center (DLR) Wessling Germany Research and Development Lab. Engineering Ingegneria Informatica S.p.A Rome Italy

This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing and computational resources for minimizing the environmental/technology footprint of the solution. A typical smart city computing continuum consists of statically installed multimodal sensing Internet-of-Things (IoT) nodes at various city locations, accompanied by interconnected computational Cloud/Edge/IoT nodes. This paper presents Optimal Trustworthy EdgeAI (OTE), an entirely novel research pipeline, that complements existing smart city infrastructure with intelligent drone Edge/IoT nodes (in the form of modularly equipped unmanned aerial vehicles), capable of autonomous repositioning according to individual/collective sensing and coverage criteria. Thereby, we envisage the emerging cutting-edge technologies of trustworthy sensing, perceiving, modelling technologies for predicting the behavior of moving targets (e.g., citizens/vehicles/objects), understanding natural phenomena (e.g., sea wave motion, urban flora/fauna, biodiversity) in order to anticipate events (people's bad habits, environmental changes), by exploiting novel continuous data processing services across the whole span of the enhanced Cloud-Edge-IoT computing continuum.

关键词： Cloud computing Privacy Smart cities Pipelines Semantics Robot sensing systems Software

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Stonefish: An Advanced Open-Source Simulation Tool Designed for Marine robotics, With a ROS Interface

Stonefish: An Advanced Open-Source Simulation Tool Designed ...

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OCEANS - Europe

作者： Patryk Cieślak Computer Vision and Robotics Research Institute Scientific and Technological Park of the University of Girona Girona Spain

The marine robotics community is lacking a high quality simulator for doing scientific research, especially when it comes to testing control and vision algorithms in realistic underwater intervention tasks. All of the solutions used today are either outdated or try to combine different software tools, which often results in bad performance, stability issues and lack of important features. This paper presents a new software tool, focused on, but not limited to, simulation of intervention autonomous underwater vehicles (I-AUV). It delivers advanced hydrodynamics based on actual geometry, simulation of underwater sensors and actuators, as well as realistic rendering of underwater environment and ocean surface. It consists of a library written in C++ and a Robot Operating System (ROS) package.

关键词： Atmospheric modeling Libraries Robot sensing systems Actuators Rendering (computer graphics)

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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Learning Context-Based Non-local Entropy Modeling for Image Compression

arXiv

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arXiv 2020年

作者： Timofte, Radu Zhang, David Zuo, Wangmeng Zhang, Kai Li, Mu School of Science and Engineering Chinese University of Hong Kong ShenzhenGuangdong518172 China Computer Vision Laboratory ETH Zurich Zurich Switzerland School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Shenzhen Research Institute of Big Data Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

The entropy of the codes usually serves as the rate loss in the recent learned lossy image compression methods. Precise estimation of the probabilistic distribution of the codes plays a vital role in the performance. However, existing deep learning based entropy modeling methods generally assume the latent codes are statistically independent or depend on some side information or local context, which fails to take the global similarity within the context into account and thus hinder the accurate entropy estimation. To address this issue, we propose a non-local operation for context modeling by employing the global similarity within the context. Specifically, we first introduce the proxy similarity functions and spatial masks to handle the missing reference problem in context modeling. Then, we combine the local and the global context via a non-local attention block and employ it in masked convolutional networks for entropy modeling. The entropy model is further adopted as the rate loss in a joint rate-distortion optimization to guide the training of the analysis transform and the synthesis transform network in transforming coding framework. Considering that the width of the transforms is essential in training low distortion models, we finally produce an U-Net block in the transforms to increase the width with manageable memory consumption and time complexity. Experiments on Kodak and Tecnick datasets demonstrate the superiority of the proposed context-based non-local attention block in entropy modeling and the U-Net block in low distortion compression against the existing image compression standards and recent deep image compression models. Copyright © 2020, The Authors. All rights reserved.

关键词： Image compression

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SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction

SR-LSTM: State Refinement for LSTM towards Pedestrian Trajec...

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IEEE/CVF Conference on computer vision and Pattern Recognition

作者： Pu Zhang Wanli Ouyang Pengfei Zhang Jianru Xue Nanning Zheng Institute of Artificial Intelligence and Robotics Xian Jiaotong University The University of Sydney SenseTime Computer Vision Research Group

ISBN: (纸本)9781728132945

In crowd scenarios, reliable trajectory prediction of pedestrians requires insightful understanding of their social behaviors. These behaviors have been well investigated by plenty of studies, while it is hard to be fully expressed by hand-craft rules. Recent studies based on LSTM networks have shown great ability to learn social behaviors. However, many of these methods rely on previous neighboring hidden states but ignore the important current intention of the neighbors. In order to address this issue, we propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism. To effectively extract the social effect of neighbors, we further introduce a social-aware information selection mechanism consisting of an element-wise motion gate and a pedestrian-wise attention to select useful message from neighboring pedestrians. Experimental results on two public datasets, i.e. ETH and UCY, demonstrate the effectiveness of our proposed SR-LSTM and we achieve state-of-the-art results.

关键词： Pedestrians Neighbor Social Behavior trajectory Refining socio-economic effects current status

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SR-LSTM: State refinement for LSTM towards pedestrian trajectory prediction

arXiv

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arXiv 2019年

作者： Zhang, Pu Ouyang, Wanli Zhang, Pengfei Xue, Jianru Zheng, Nanning Institute of Artificial Intelligence and Robotics Xian Jiaotong University China University of Sydney SenseTime Computer Vision Research Group Australia

关键词： Iterative methods

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Interconnected robotic platforms inform deep-sea ecological restoration trends

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Marine pollution bulletin 2025年 219卷

作者： Ivan Masmitja Narcís Palomeras Daniel M Toma Nixon Bahamon Matias Carandell José A García Del Arco Damianos Chatzievangelou Marta Real Antoni Sánchez-Márquez Gerard Batet Pau Vial Francisco Bonin-Font Caterina Muntaner Natàlia Hurtós Juan M López Alex R Ripoll Guillermo de Arcas Jordi Grinyó Spartacus Gomariz Kakani Katija Jacopo Aguzzi Marc Carreras Joaquin Del-Rio Joan Navarro Joan B Company Institut de Ciències del Mar (ICM) CSIC Barcelona 08003 Spain. Electronic address: masmitja@icm.csic.es. Computer vision and Robotics Institute Universitat de Girona (UdG) Girona 17003 Spain. SARTI-MAR Research group Universitat Politècnica de Catalunya (UPC) Barcelona Tech. Barcelona 08034 Spain. Institut de Ciències del Mar (ICM) CSIC Barcelona 08003 Spain. Systems Robotics and Vision Group University of the Balearic Islands (UIB) Palma de Mallorca 07122 Spain. IQUA Robotics Girona 17003 Spain. Universidad Politécnica de Madrid (UPM) Grupo de Investigación en Instrumentación y Acústica Aplicada Madrid 28031 Spain. Research and Development Bioinspiration Lab Monterey Bay Aquarium Research Institute (MBARI) Moss Landing CA 95062 USA. Institut de Ciències del Mar (ICM) CSIC Barcelona 08003 Spain Institut Català de Recerca per a la Governança del Mar (ICATMAR) Barcelona 08003 Spain.

The implementation of Marine Protected Areas (MPAs) has increased in the last few decades, creating a deep impact on ocean governance and local fishery communities. Despite their effectiveness being actively debated, initial results show that this spatial management could promote the recovery of depleted marine communities and species, and ecosystem services such as increased fishing profits, especially in nearby areas. This could potentially maintain the economic levels of local and regional under-pressure economies while improving the overall marine biodiversity. However, the lack of ecological monitoring systems for long-lasting, non-invasive, and multiparametric measurements in near-real-time is a roadblock to national-level monitoring programs in those areas. Here we present a set of technological solutions, including robotics, biologging, and machine learning approaches to monitor MPAs, developed and validated in different deep-sea field experiments. The outcomes of these trials have yielded insights into the fine-scale behavioral ecology of different deep-sea species and the recovery dynamics of overexploited seafloors. With these tools, it is possible to monitor restored areas via ecological indicators, that are key to informing and empowering policymakers, conservation biologists, and fishery ecology experts, propelling an effective approach to the conservation and governance of marine ecosystems.

关键词： Acoustics Autonomous vehicles Conservation Ecosystem services Machine learning Marine protected areas Ocean monitoring Restoration robotics Underwater technology

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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