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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Ahmed, Habib Son Thanh Nguyen Duc La Chuong Phuoc Le Hung Manh La Univ Nevada Dept Comp Sci & Engn Adv Robot & Automat ARA Lab Reno NV 89557 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

This paper presents a novel design of a multidirectional bicycle robot, which is developed for the inspection of steel structures, in particular, steel-reinforced bridges. The locomotion concept is based on arranging two magnetic wheels in a bicycle-like configuration with two independent steering actuators. This configuration allows the robot to possess multidirectional mobility. An additional free joint helps the robot adapt naturally to non-flat and complex steel structures. The robot's design provides the advantage of being mechanically simple and providing high-level mobility across diverse steel structures. In addition, a visual sensor is equipped that allows the data collection for steel defect detection with offline training and validation. The paper also provides a novel pipeline for Steel Defect Detection, which utilizes multiple datasets (one for training and one for validation) from real bridges. The quantitative results have been reported for three Deep Encoder-Decoder Networks (i.e., LinkNet, UNet, DeepLab) with their corresponding Encoder modules (i.e., ResNet-18, ResNet-34, RegNet-X2, EfficientNet-B0, and EfficientNet-B2). Due to space concerns, the qualitative results have been outlined in Appendix, with a link in Fig. 11 caption to access the result provided.

关键词： Bridges Training visualization Pipelines Wheels Bicycles Inspection

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Towards Autonomous Control of Surgical Instruments using Adaptive-Fusion Tracking and Robot Self-Calibration

Towards Autonomous Control of Surgical Instruments using Ada...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Wang, Chiyu Cartucho, Joao Elson, Daniel Darzi, Ara Giannarou, Stamatia Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

The ability to track surgical instruments in real-time is crucial for autonomous Robotic Assisted Surgery (RAS). Recently, the fusion of visual and kinematic data has been proposed to track surgical instruments. However, these methods assume that both sensors are equally reliable, and cannot successfully handle cases where there are significant perturbations in one of the sensors' data. In this paper, we address this problem by proposing an enhanced fusion-based method. The main advantage of our method is that it can adjust fusion weights to adapt to sensor perturbations and failures. Another problem is that before performing an autonomous task, these robots have to be repetitively recalibrated by a human for each new patient to estimate the transformations between the different robotic arms. To address this problem, we propose a self-calibration algorithm that empowers the robot to autonomously calibrate the transformations by itself in the beginning of the surgery. We applied our fusion and self-calibration algorithms for autonomous ultrasound tissue scanning and we showed that the robot achieved stable ultrasound imaging when using our method. Our performance evaluation shows that our proposed method outperforms the state-of-art both in normal and challenging situations.

关键词： visualization Ultrasonic imaging Instruments Perturbation methods Surgery Sensor fusion Robot sensing systems

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Playful Interactions for Representation Learning

Playful Interactions for Representation Learning

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Young, Sarah Pari, Jyothish Abbeel, Pieter Pinto, Lerrel Univ Calif Berkeley Berkeley CA 94720 USA NYU New York NY 10003 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

One of the key challenges in visual imitation learning is collecting large amounts of expert demonstrations for a given task. While methods for collecting human demonstrations are becoming easier with teleoperation methods and the use of low-cost assistive tools, we often still require 100-1000 demonstrations for every task to learn a visual representation and policy. To address this, we turn to an alternate form of data that does not require task-specific demonstrations play. Playing is a fundamental method children use to learn a set of skills and behaviors and visual representations in early learning. Importantly, play data is diverse, task-agnostic, and relatively cheap to obtain. In this work, we propose to use playful interactions in a self-supervised manner to learn visual representations for downstream tasks. We collect 2 hours of playful data in 19 diverse environments and use self-predictive learning to extract visual representations. Given these representations, we train policies using imitation learning for two downstream tasks: Pushing and Stacking. We demonstrate that our visual representations generalize better than standard behavior cloning and can achieve similar performance with only half the number of required demonstrations. Our representations, which are trained from scratch, compare favorably against ImageNet pretrained representations. Finally, we provide an experimental analysis on the effects of different pretraining modes on downstream task learning.

关键词： Representation learning visualization Stacking Cloning Behavioral sciences Data mining Task analysis

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Models and algorithms for analyzing chatbot and user data in real-time systems

Models and algorithms for analyzing chatbot and user data in...

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2022 International conference on information Science and Communications Technologies, ICISCT 2022

作者： Urazmatov, Tokhir Khujaev, Otabek Kuzibaev, Khudayshukur Urgench Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Information Technology Department Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi It Department Urgench Uzbekistan

ISBN: (纸本)9781665472296

Big data technologies are used to achieve any type of analysis in a fast and predictable manner, thereby improving decision-making at the human and machine level. The principles of distributed computing are the keys to big data technologies and analytics. Engines related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing on multiple low-cost machines, fixed price for human and machine consumption of big data analysis and are the key issues that allow for implementation in time. © 2022 ieee.

关键词： Big data

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What You Say Is Not What You Do: Studying Visio-Linguistic Models for TV Series Summarization 18

What You Say Is Not What You Do: Studying Visio-Linguistic M...

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18th ieee/CVF International conference on Computer Vision (ICCV)

作者： Reboud, Alison Troncy, Raphael EURECOM Sophia Antipolis France

ISBN: (纸本)9781665401913

In this paper, we generate TV series summaries using both visual cues present in video frames and screenplay (dialogue and scenic textual descriptions). Recently, approaches relying on pre-trained vision and language representations have proven to be successful for several downstream tasks using paired text and images. For TV series summarization, we hypothesize that both scenic information and dialogues are useful to generate summaries. Visiolinguistic models being presented as task-agnostic, we explore if and how they can be used for TV series summarization by conducting experiments with varying text inputs and models fine-tuned on different datasets. We observe that such generic models, despite not being specifically designed for narrative understanding, achieve results closed to the state of the art. Our results suggest also that non aligned data also benefit from this type of visio-linguistics architecture.

关键词： visualization Computer vision TV conferences Computational modeling Data visualization Computer architecture

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Back to the Manifold: Recovering from Out-of-Distribution States

Back to the Manifold: Recovering from Out-of-Distribution St...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Reichlin, Alfredo Marchetti, Giovanni Luca Yin, Hang Ghadirzadeh, Ali Kragic, Danica KTH Royal Inst Technol Stockholm Sweden Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Learning from previously collected datasets of expert data offers the promise of acquiring robotic policies without unsafe and costly online explorations. However, a major challenge is a distributional shift between the states in the training dataset and the ones visited by the learned policy at the test time. While prior works mainly studied the distribution shift caused by the policy during the offline training, the problem of recovering from out-of-distribution states at the deployment time is not very well studied yet. We alleviate the distributional shift at the deployment time by introducing a recovery policy that brings the agent back to the training manifold whenever it steps out of the in-distribution states, e.g., due to an external perturbation. The recovery policy relies on an approximation of the training data density and a learned equivariant mapping that maps visual observations into a latent space in which translations correspond to the robot actions. We demonstrate the effectiveness of the proposed method through several manipulation experiments on a real robotic platform. Our results show that the recovery policy enables the agent to complete tasks while the behavioral cloning alone fails because of the distributional shift problem.

关键词： Training Manifolds visualization Perturbation methods Training data Cloning Behavioral sciences

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Neural Flow Map Reconstruction

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COMPUTER GRAPHICS FORUM 2022年第3期41卷 391-402页

作者： Sahoo, S. Lu, Y. Berger, M. Vanderbilt Univ Nashville TN 37235 USA

In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time-varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generate in situ that are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time-varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem - learning a function-space neural network to reproduce flow map samples under a fixed integration scheme - leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.

关键词： CCS Concepts • Computing methodologies → Neural networks Reconstruction • Human-centered computing → Scientific visualization

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HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture

HVH: Learning a Hybrid Neural Volumetric Representation for ...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Ziyan Nam, Giljoo Stuyck, Tuur Lombardi, Stephen Zollhofer, Michael Hodgins, Jessica Lassner, Christoph Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta AI New York NY USA Real Labs Res New York NY 10003 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance. Yet, hair is a critical component for believable avatars. In this paper, we address the aforementioned problems: 1) we use a novel, volumetric hair representation that is composed of thousands of primitives. Each primitive can be rendered efficiently, yet realistically, by building on the latest advances in neural rendering. 2) To have a reliable control signal, we present a novel way of tracking hair on the strand level. To keep the computational effort manageable, we use guide hairs and classic techniques to expand those into a dense hood of hair. 3) To better enforce temporal consistency and generalization ability of our model, we further optimize the 3D scene flow of our representation with multiview optical flow, using volumetric raymarching. Our method can not only create realistic renders of recorded multi-view sequences, but also create renderings for new hair configurations by providing new control signals. We compare our method with existing work on viewpoint synthesis and drivable animation and achieve state-of-the-art results. https://***/hvh/

关键词： Hair Computer vision visualization Solid modeling Image motion analysis Three-dimensional displays Rendering (computer graphics)

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Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture

Towards General Purpose Vision Systems: An End-to-End Task-A...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Gupta, Tanmay Kamath, Amita Kembhavi, Aniruddha Hoiem, Derek PRIOR Allen Inst AI Seattle WA 98103 USA Univ Illinois Urbana IL 61801 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Computer vision systems today are primarily N-purpose systems, designed and trained for a predefined set of tasks. Adapting such systems to new tasks is challenging and often requires non-trivial modifications to the network architecture (e.g. adding new output heads) or training process (e.g. adding new losses). To reduce the time and expertise required to develop new applications, we would like to create general purpose vision systems that can learn and perform a range of tasks without any modification to the architecture or learning process. In this paper, we propose GPV-1, a task-agnostic vision-language architecture that can learn and perform tasks that involve receiving an image and producing text and/or bounding boxes, including classification, localization, visual question answering, captioning, and more. We also propose evaluations of generality of architecture, skill-concept' transfer, and learning efficiency that may inform future work on general purpose vision. Our experiments indicate GPV-1 is effective at multiple tasks, reuses some concept knowledge across tasks, can perform the Referring Expressions task zero-shot, and further improves upon the zero-shot performance using a few training samples.

关键词： Training Computer vision visualization Machine vision Computer architecture Object detection Network architecture

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Automatic patch installation method of operating system based on deep learning 5

Automatic patch installation method of operating system base...

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5th ieee information Technology, Networking, Electronic and Automation Control conference, ITNEC 2021

作者： Zhang, Kunsan Chen, Chen Lin, Nan Zeng, Zhen Fu, Shichen State Grid Zhangzhou Power Supply Company Zhangzhou363000 China State Grid Putian Power Supply Company Putian351100 China

ISBN: (纸本)9781665415989

In order to improve the security and reliability of information system and reduce the risk of vulnerability intrusion and attack, an automatic patch installation method of operating systems based on deep learning is proposed, If the installation is successful, the basic information of the system will be returned to the visualization server. If the installation fails, it is recommended to upgrading manually and display it on the patch detection visualization server. Through the practical application of statistical analysis, the statistical results show that the proposed method is significantly better than the original and traditional installation methods, which can effectively avoid the problem of client repeated download, and greatly improve the success rate of patch automatic upgrades. It effectively saves the upgrade cost and ensures the security and reliability of the information system. © 2021 ieee.

关键词： visualization

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