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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Finding Fallen Objects Via Asynchronous Audio-Visual Integration

Finding Fallen Objects Via Asynchronous Audio-Visual Integra...

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

作者： Gan, Chuang Gu, Yi Zhou, Siyuan Schwartz, Jeremy Alter, Seth Traer, James Gutfreund, Dan Tenenbaum, Joshua B. McDermott, Josh H. Torralba, Antonio MIT 77 Massachusetts Ave Cambridge MA 02139 USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The way an object looks and sounds provide complementary reflections of its physical properties. In many settings cues from vision and audition arrive asynchronously but must be integrated, as when we hear an object dropped on the floor and then must find it. In this paper, we introduce a setting in which to study multi-modal object localization in 3D virtual environments. An object is dropped somewhere in a room. An embodied robot agent, equipped with camera and microphone, must determine what object has been dropped - and where - by combining audio and visual signals with knowledge of the underlying physics. To study this problem, we have generated a large-scale dataset - the Fallen Objects dataset - that includes 8000 instances of 30 physical object categories in 64 rooms. The dataset uses the ThreeDWorld Platform that can simulate physics-based impact sounds and complex physical interactions between objects in a photorealistic setting. As a first step toward addressing this challenge, we develop a set of embodied agent baselines, based on imitation learning, reinforcement learning, and modular planning, and perform an in-depth analysis of the challenge of this new task. This dataset is publicly available(1).

关键词： Location awareness visualization Three-dimensional displays Robot vision systems Virtual environments Reinforcement learning Reflection

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Real Image Super-Resolution using GAN through modeling of LR and HR process 18

Real Image Super-Resolution using GAN through modeling of LR...

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18th ieee International conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Umer, Rao Muhammad Micheloni, Christian Helmholtz Munich Inst AI Hlth AIH Munich Germany Univ Udine Dept Math & Comp Sci Udine Italy

ISBN: (纸本)9781665463829

The current existing deep image super-resolution methods usually assume that a Low Resolution (LR) image is bicubicly downscaled of a High Resolution (HR) image. However, such an ideal bicubic downsampling process is different from the real LR degradations, which usually come from complicated combinations of different degradation processes, such as camera blur, sensor noise, sharpening artifacts, JPEG compression, and further image editing, and several times image transmission over the internet and unpredictable noises. It leads to the highly ill-posed nature of the inverse upscaling problem. To address these issues, we propose a GAN-based SR approach with learnable adaptive sinusoidal nonlinearities incorporated in LR and SR models by directly learn degradation distributions and then synthesize paired LR/HR training data to train the generalized SR model to real image degradations. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments.

关键词： Degradation Adaptation models visualization Surveillance Image communication Superresolution Training data

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Sign Language Translation with Hierarchical Spatio-Temporal Graph Neural Network 22

Sign Language Translation with Hierarchical Spatio-Temporal ...

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Kan, Jichao Hu, Kun Hagenbuchner, Markus Tsoi, Ah Chung Bennamoun, Mohammed Wang, Zhiyong Univ Sydney Sch Comp Sci Sydney NSW Australia Univ Technol Sydney Data Sci Inst Sydney NSW Australia Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia Univ Western Australia Dept Comp Sci & Software Engn Nedlands WA Australia Univ Sydney Philosophy Study Sydney NSW Australia

ISBN: (纸本)9781665409155

Sign language translation (SLT), which generates text in a spoken language from visual content in a sign language, is important to assist the hard-of-hearing community for their communications. Inspired by neural machine translation (NMT), most existing SLT studies adopted a general sequence to sequence learning strategy. However, SLT is significantly different from general NMT tasks since sign languages convey messages through multiple visual-manual aspects. Therefore, in this paper, these unique characteristics of sign languages are formulated as hierarchical spatio-temporal graph representations, including high-level and fine-level graphs of which a vertex characterizes a specified body part and an edge represents their interactions. Particularly, high-level graphs represent the patterns in the regions such as hands and face, and fine-level graphs consider the joints of hands and landmarks of facial regions. To learn these graph patterns, a novel deep learning architecture, namely hierarchical spatio-temporal graph neural network (HST-GNN), is proposed. Graph convolutions and graph self-attentions with neighborhood context are proposed to characterize both the local and the global graph properties. Experimental results on benchmark datasets demonstrated the effectiveness of the proposed method.

关键词： Deep learning visualization Gesture recognition Assistive technologies Transformers Graph neural networks Decoding

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Simple Baseline for Single Human Motion Forecasting 18

Simple Baseline for Single Human Motion Forecasting

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

作者： Wang, Chenxi Wang, Yunfeng Huang, Zixuan Chen, Zhiwen Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665401913

Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction. Visual and social information are often used to boost model performance, however, they may consume too much computational resources. In this paper, we establish a simple but effective baseline for single human motion forecasting without visual and social information, equipped with useful training tricks. Our method "futuremotion_ICCV21" outperforms existing methods by a large margin on SoMoF benchmark'. We hope our work provide new ideas for future research.

关键词： Training visualization Computer vision conferences Computational modeling Benchmark testing Trajectory

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Grounded Language-Image Pre-training

Grounded Language-Image Pre-training

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

作者： Li, Liunian Harold Zhang, Pengchuan Zhang, Haotian Yang, Jianwei Li, Chunyuan Zhong, Yiwu Wang, Lijuan Yuan, Lu Zhang, Lei Hwang, Jenq-Neng Chang, Kai-Wei Gao, Jianfeng Univ Calif Los Angeles Los Angeles CA 90024 USA Microsoft Res Redmond WA 98052 USA Univ Washington Seattle WA 98195 USA Univ Wisconsin Madison WI USA Microsoft Cloud & AI Redmond WA USA Intemat Digital Econ Acad Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper presents a grounded language-image pretraining (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model;2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representations semantic-rich. In our experiments, we pre-train GLIP on 27M grounding data, including 3M human-annotated and 24M web-crawled image-text pairs. The learned representations demonstrate strong zero-shot and few-shot transferability to various object-level recognition tasks. 1) When directly evaluated on COCO and LVIS (without seeing any images in COCO during pre-training), GLIP achieves 49.8 AP and 26.9 AP respectively, surpassing many supervised baselines.(1) 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13 downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head. Code will be released at https://***/microsoft/GLIP.

关键词： visualization Computer vision Image recognition Head Grounding Object detection Data models

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VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning

VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Le...

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

作者： Xu, Wenjia Xian, Yongqin Wang, Jiuniu Schiele, Bernt Akata, Zeynep Beijing Univ Posts & Telecommun Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland Max Planck Inst Informat Saarbrucken Germany Univ Tubingen Tubingen Germany City Univ Hong Kong Hong Kong Peoples R China Max Planck Inst Intelligent Syst Stuttgart Germany Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-annotated attributes serve as powerful semantic embeddings in zero-shot learning. However, their annotation process is labor-intensive and needs expert supervision. Current unsupervised semantic embeddings, i.e., word embeddings, enable knowledge transfer between classes. However, word embeddings do not always reflect visual similarities and result in inferior zero-shot performance. We propose to discover semantic embeddings containing discriminative visual properties for zero-shot learning, without requiring any human annotation. Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class discrimination and semantic relatedness. To associate these clusters with previously unseen classes, we use external knowledge, e.g., word embeddings and propose a novel class relation discovery module. Through quantitative and qualitative evaluation, we demonstrate that our model discovers semantic embeddings that model the visual properties of both seen and unseen classes. Furthermore, we demonstrate on three benchmarks that our visually-grounded semantic embeddings further improve performance over word embeddings across various ZSL models by a large margin. Code is available at https://***/wenjiaXu/VGSE

关键词： visualization Computer vision Codes Annotations Semantics Benchmark testing Predictive models

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IoT Empowered Open Sensor Network for Environmental Air Pollution Monitoring System in Smart Cities 8

IoT Empowered Open Sensor Network for Environmental Air Poll...

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8th International conference on information Technology Research, ICITR 2023

作者： Sudantha, Bh Manchanayaka, Malsk Yang, Chan-Yun Premachandra, C. Firdhous, Mfm Sumathipala, Kasn University of Moratuwa Department of Information Technology Sri Lanka National Taipei University Department of Electrical Engineering New Taipei City23741 Taiwan Shibaura Institute of Technology Department of Electronics Engineering Koto-ku Japan University of Moratuwa Department of Computational Mathematics Sri Lanka

ISBN: (纸本)9798350359503

Designing of an IoT-based air pollution monitoring system is a proactive and impactful approach to tackle the critical issue of air pollution and its adverse effects on the environment. Such a system is instrumental in offering real-time air quality data, empowering swift responses when pollution levels exceed acceptable thresholds. The system's design encompasses various hardware components, including carefully chosen air quality sensors, a microcontroller (Arduino Mega 2560), a communication module for the network connectivity, and a sustainable power supply. On the software front, the system involves data collection from sensors, data processing with calibration and conversion to air quality parameters, secure data transmission, storage, analysis, and an alerting mechanism. User interface development facilitates real-time and historical data visualization, while stringent security measures ensure data protection. Scalability, integration, and strategic deployment in key areas further enhance the system's effectiveness. Regular maintenance and updates are vital to ensuring accurate and reliable performance. Overall, implementing an IoT-based air pollution monitoring system is an essential step toward monitoring and addressing air quality concerns in a comprehensive and timely manner, ultimately fostering environmental awareness, and driving pollution mitigation efforts. © 2023 ieee.

关键词： Digital storage

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Balanced Scorecard and Six Sigma Integration in IT Project Management, Digital Processes and Products Design

Balanced Scorecard and Six Sigma Integration in IT Project M...

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ieee conference on Quality Management, Transport and information Security, information Technologies (IT&MQ&IS)

作者： Vladimir N. Azarov Elena L. Kuzina Marina A. Kuzina Anatoliy V. Chekmarev Department of Quality Management Russian University of Transport (MIIT) Moscow Russia Department of Economics and Banking Business MGIMO University Moscow Russia

ISBN: (数字)9798350367522

ISBN: (纸本)9798350367539

The integration of the Balanced Scorecard (BSC) and Six Sigma methodology allows organizations to enhance their efficiency and quality. While the BSC defines key strategic objectives, Six Sigma utilizes statistical tools to identify and eliminate sources of defects that impede the achievement of these objectives. The amalgamation of these approaches yields numerous benefits, including improved planning, enhanced customer value, increased profitability, and heightened competitiveness. To effectively visualize multidimensional data and results of the integration, it is recommended to employ three-dimensional radar charts created using data visualization tools such as Tableau, Power BI, or Python. These charts provide a visual representation of information, facilitating informed decision-making by individuals responsible for strategic planning and quality management. In a specific example of a mobile application, the BSC can be used to define key goals (brand awareness, number of downloads, user experience quality), while Six Sigma is employed to identify and eliminate defects in the application's design, functionality, and performance.

关键词： visualization Three-dimensional displays Profitability Data visualization Project management Radar Strategic planning User experience Six sigma Quality management

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A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts

A Large-Scale Comparison of Python Code in Jupyter Notebooks...

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19th International conference on Mining Software Repositories (MSR)

作者： Grotov, Konstantin Titov, Sergey Sotnikov, Vladimir Golubev, Yaroslav Bryksin, Timofey JetBrains Res Boston MA 02210 USA ITMO Univ St Petersburg Russia

ISBN: (纸本)9781450393034

In recent years, Jupyter notebooks have grown in popularity in several domains of software engineering, such as data science, machine learning, and computer science education. Their popularity has to do with their rich features for presenting and visualizing data, however, recent studies show that notebooks also share a lot of drawbacks: high number of code clones, low reproducibility, etc. In this work, we carry out a comparison between Python code written in Jupyter Notebooks and in traditional Python scripts. We compare the code from two perspectives: structural and stylistic. In the first part of the analysis, we report the difference in the number of lines, the usage of functions, as well as various complexity metrics. In the second part, we show the difference in the number of stylistic issues and provide an extensive overview of the 15 most frequent stylistic issues in the studied mediums. Overall, we demonstrate that notebooks are characterized by the lower code complexity, however, their code could be perceived as more entangled than in the scripts. As for the style, notebooks tend to have 1.4 times more stylistic issues, but at the same time, some of them are caused by specific coding practices in notebooks and should be considered as false positives. With this research, we want to pave the way to studying specific problems of notebooks that should be addressed by the development of notebook-specific tools, and provide various insights that can be useful in this regard.

关键词： Codes Data visualization Machine learning Data science Software Reproducibility of results Encoding

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LieVis: A Visual Interactive Dashboard for Lie Detection Using Machine Learning and Deep Learning Techniques 26

LieVis: A Visual Interactive Dashboard for Lie Detection Usi...

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26th International conference on Computer and information Technology, ICCIT 2023

作者： Prome, Shanjita Akter Rafiqul Islam, Md Asirvatham, David Hossain Sakib, Md. Kowsar Ari Ragavan, Neethiahnanthan Australian Institute of Higher Education Business Information Systems Australia Taylors University School of Computer Science Malaysia Taylors University Faculty of Social Sciences & Leisure Management Malaysia

ISBN: (纸本)9798350359015

Lying poses a significant concern in our everyday lives, impacting social interactions. The skill of detecting lies or deceitful statements is of immense value, mainly due to the elusive nature of the underlying patterns associated with lying. Numerous studies have extensively explored lie detection, employing various advanced techniques. Researchers have devoted their attention to devising more effective and efficient solutions for lie detection. However, there remain ongoing challenges, particularly in achieving higher accuracy levels. There has been relatively limited focus on developing visually interpretable and interactive systems for lie detection. Therefore, we leveraged artificial intelligence (AI), incorporating machine learning and deep learning techniques to augment the existing system. Our experiment shows that deep learning models, specifically BERT, achieve the highest accuracy, whereas machine learning models such as Random Forest (RF), and K-Nearest Neighbors (KNN) exhibit superior accuracy across both sets of features. To enhance result exploration, we introduced a dedicated visual interactive system/dashboard called LieVis, meticulously designed to facilitate comprehensive exploration of the obtained results. The evaluation of LieVis showcases its superiority over existing systems. The source code and the live dashboard with result visualization are available on our GitHub repository https://***/KHSakib/LieVis/tree/master. © 2023 ieee.

关键词： Deep Learning Lie Detection Machine Learning Visual Dashboard

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