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Open-Ended Online Learning for Autonomous Visual Perception

ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 10178-10198页

作者： Yu, Haibin Cong, Yang Sun, Gan Hou, Dongdong Liu, Yuyang Dong, Jiahua Chinese Acad Sci Shenyang Inst Automation Inst Robot & Intelligent Mfg State Key Lab Robot Shenyang Peoples R China Natl Univ Def Technol Hefei Peoples R China

The visual perception systems aim to autonomously collect consecutive visual data and perceive the relevant information online like human beings. In comparison with the classical static visual systems focusing on fixed tasks (e.g., face recognition for visual surveillance), the real-world visual systems (e.g., the robot visual system) often need to handle unpredicted tasks and dynamically changed environments, which need to imitate human-like intelligence with open-ended online learning ability. Therefore, we provide a comprehensive analysis of open-ended online learning problems for autonomous visual perception in this survey. Based on "what to online learn " among visual perception scenarios, we classify the open-ended online learning methods into five categories: instance incremental learning to handle data attributes changing, feature evolution learning for incremental and decremental features with the feature dimension changed dynamically, class incremental learning and task incremental learning aiming at online adding new coming classes/tasks, and parallel and distributed learning for large-scale data to reveal the computational and storage advantages. We discuss the characteristic of each method and introduce several representative works as well. Finally, we introduce some representative visual perception applications to show the enhanced performance when using various open-ended online learning models, followed by a discussion of several future directions.

关键词： Task analysis Machine vision Learning systems Visual perception data models visualization Training Incremental learning lifelong learning online learning open-ended visual perception

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EO-VALIDATION: LOW LATENCY COMMODITY-BASED COLLABORATIVE VALIDATION FRAMEWORK FOR GEOAI data PRODUCTS

EO-VALIDATION: LOW LATENCY COMMODITY-BASED COLLABORATIVE VAL...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Caraballo-Vega, Jordan A. Spradlin, Caleb S. Carroll, Mark Neigh, Christopher S. R. Wooten, Margaret Wessels, Konrad Minh Tri Le Strong, Savannah L. Frost, Melanie Burke, Amanda Alemu, Woubet Diouf, Abdoul Aziz Mbaye, Modou Ndao, Babacar Thomas, Nathan Brown, Molly NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA NASA GSFC Sci Syst & Applicat Inc SSAI Greenbelt MD USA Edge Hill Univ Ormskirk Lancs England Univ Maryland College Pk MD 20742 USA Ctr Suivi Ecol Dakar Senegal Agr Res Inst Thies Senegal George Mason Univ Fairfax VA 22030 USA InuTeq LLC Beltsville MD 20705 USA

ISBN: (纸本)9798350360332;9798350360325

The proliferation of machine learning models, architectures, and datasets for Earth observation (EO) continues to rise dramatically. This pattern is expected to continue growing bringing with it an increase in the generation of remote sensing derived data products powered by geospatial artificial intelligence (GeoAI) techniques. Rigorous quality assessments and accuracy analysis needs to be undertaken for the science community to adopt many of these data products for scientific discovery of changes of the Earth's land surface. While there is existing literature supporting and documenting best practices for the validation of GeoAI data products, the software to support large-scale collaborative validation efforts is limited. In this study we present the design and software implementation of a flexible commodity-based framework for large-scale global to regional validation of GeoAI data products. This framework's main purpose is to enable, speed up, and optimize the acquisition of validation data for large-scale science projects with support across multiple sensors and spatial resolutions with little to no code. In addition, we present several use cases where this framework has enabled and streamlined the validation of global to regional data products at different spatial resolutions and within different computational platforms.

关键词： remote sensing machine learning deep learning validation land cover land use

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LOOPS: LOcally Optimized Polygon Simplification

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COMPUTER GRAPHICS FORUM 2022年第3期41卷 355-365页

作者： Amiraghdam, Alireza Diehl, Alexandra Pajarola, Renato Univ Zurich Dept Informat Zurich Switzerland

Displaying polygonal vector data is essential in various application scenarios such as geometry visualization, vector graphics rendering, CAD drawing and in particular geographic, or cartographic visualization. Dealing with static polygonal datasets that has a large scale and are highly detailed poses several challenges to the efficient and adaptive display of polygons in interactive geographic visualization applications. For linear vector data, only recently a GPU-based level-of-detail (LOD) polyline simplification and rendering approach has been presented which can perform locally-adaptive LOD visualization of large-scale line datasets interactively. However, locally optimized LOD simplification and interactive display of large-scale polygon data, consisting of filled vector line loops, remains still a challenge, specifically in 3D geographic visualizations where varying LOD over a scene is necessary. Our solution to this challenge is a novel technique for locally-optimized simplification and visualization of 2D polygons over a 3D terrain which features a parallelized point-inside-polygon testing mechanism. Our approach is capable of employing any simplification algorithm that sequentially removes vertices such as Douglas-Peucker and Wang-Muller. Moreover, we generalized our technique to also visualizing polylines in order to have a unified method for displaying both data types. The results and performance analysis show that our new algorithm can handle large datasets containing polygons composed of millions of segments in real time, and has a lower memory demand and higher performance in comparison to prior methods of line simplification and visualization.

关键词： CCS Concepts • Human-centered computing → Geographic visualization visualization techniques • Theory of computation → Computational geometry • Computing methodologies → Rendering Rasterization

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Design of Soil Pollution Prevention and Control Evaluation and analysis Platform based on WebGIS 4

Design of Soil Pollution Prevention and Control Evaluation a...

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4th International symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Liu, Derong Liang, Hong Gao, Kun Yunnan University School of Information Science and Technology Yunnan Kunming China

ISBN: (纸本)9798350354560

This study focuses on heavy metal pollution in Yunnan Province and designed and developed a data informatization platform for soil environmental quality monitoring and data informatization for pollution control. The platform integrates a large amount of existing heterogeneous data resources from multiple sources, enabling WebGIS visualization of soil environmental quality and pollution, data analysis and graph presentation, thematic mapping and evaluation analysis report generation, and functions for assessing soil environmental quality and determining the causes of agricultural pollution and building areas [1]. By adopting the latest technological architecture such as NodeJS, VueJS and Cesium, the platform achieves efficient data integration, transmission, analysis and visualization, improving the efficiency of analysis, monitoring and control of soil pollution data. The platform uses the single-factor method, the comprehensive Nemerow index method and the analytical hierarchy process as soil pollution assessment methods, combined with coordinated monitoring of heavy metal content in agricultural products, making the assessment results more comprehensive and accurate. This system will help improve soil environmental quality monitoring and pollution control capabilities in Yunnan Province by providing comprehensive and accurate baseline data and scientific decision support to government agencies. © 2024 ieee.

关键词： Soil pollution control

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A Hierarchical Coarse-to-Fine Fault Diagnosis Method for Industrial Processes Based on Decision Fusion of Class-Specific Stacked Autoencoders

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Gao, Huihui Zhang, Xiaoran Gao, Xuejin Li, Fangyu Han, Honggui Beijing Univ Technol Engn Res Ctr Digital Community Minist Educ Sch Informat Sci & Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China

Fault diagnosis (FD) is crucial for ensuring the safety and stability of industrial processes. In real industrial processes, fault features in measurement data are prone to be misidentified due to the feature overlap, which hinders the effective FD. Besides, the large-scale characteristic of industrial processes also poses challenges for FD. To solve the above problems, a hierarchical coarse-to-fine FD method based on decision fusion of class-specific stacked autoencoders (DFCSSAEs) is proposed to achieve accurate FD by identifying overlapping features. The entire FD process is divided into three stages: sub-block division, coarse diagnosis, and fine diagnosis. In the sub-block division stage, for the large-scale characteristic, a sub-block division method based on prior knowledge and mutual information (MI) is proposed to divide the whole process into several new sub-blocks. On this basis, the faults with overlapping features in each sub-block are identified by leveraging the feature visualization technique and summarized into a new composite class. In the coarse diagnosis stage, several SAE-Softmax-based coarse FD models are established to achieve the targeted diagnosis of individual faults and the composite class fault. At the fine diagnosis stage, a weighted Dempster-Shafer (D-S) evidence theory is proposed to solve decision conflicts among different coarse FD models by assigning reasonable weights. Moreover, a new SAE-Softmax is established to diagnose the unclassifiable faults after the decision fusion and ultimately improve diagnostic accuracy. Finally, the effectiveness and advantages of our method are validated by the Tennessee Eastman process (TEP) and the real dataset of the PRONTO process. Experimental results demonstrate that our method achieves high FD rates of 0.962 and 0.997.

关键词： Fault diagnosis Feature extraction Accuracy Evidence theory Numerical models Principal component analysis Mutual information Dempster-Shafer (D-S) evidence theory fault diagnosis (FD) industrial process large scale stacked autoencoder (SAE)

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Efficient Interactive Global Cellular Signal Strength visualization

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ieee TRANSACTIONS ON BIG data 2022年第5期8卷 1209-1219页

作者： Ma, Mengyu Chen, Luo Ouyang, Xue Liu, Xiaoran Li, Jun Jing, Ning Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

Cellular Signal Strength (CSS), defined as the signal power received by mobile phones, is an important aspect of geographic information flow analysis, because the density of such information can reflect the urbanization variables such as population, gross domestic product, built-up area, electric power consumption, etc. Despite the importance, the real-time analysis of global CSS distribution remains a challenging problem due to the large data scale. In this article, a Display-driven Computing (DisDC) technique is designed and applied to provide efficient large scale interactive CSS visualization, generating results by calculating the value of each pixel that directly for display. Specifically, we present an efficient CSS measurement algorithm, which introduces spatial indexes and a corresponding query strategy;besides, an optimized parallel computing architecture is proposed to ensure the ability of real-time visualization. Experiments show that our approach obviously outperforms traditional methods and is capable of handling more than 40 million base stations in real-time. Moreover, an online demonstration is provided at https://***/MemoryMmy/CSSMap.

关键词： Cascading style sheets data visualization Base stations Real-time systems Big data Spatial databases visualization Cellular signal strength spatial analysis real-time big data parallel computing

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MAD-Max Beyond Single-Node: Enabling large Machine Learning Model Acceleration on Distributed Systems 51

MAD-Max Beyond Single-Node: Enabling Large Machine Learning ...

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ACM/ieee 51st Annual International symposium on Computer Architecture (ISCA)

作者： Hsia, Samuel Golden, Alicia Acun, Bilge Ardalani, Newsha DeVito, Zachary Wei, Gu-Yeon Brooks, David Wu, Carole-Jean FAIR Meta Menlo Pk CA 94025 USA Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9798350326598;9798350326581

Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our analysis, grounded in real-world large model training on datacenter-scale infrastructures, reveals that 14 similar to 32% of all GPU hours are spent on communication with no overlapping computation. To minimize this outstanding communication latency and other inherent at-scale inefficiencies, we introduce an agile performance modeling framework, MAD-Max. This framework is designed to optimize parallelization strategies and facilitate hardware-software co-design opportunities. Through the application of MAD-Max to a suite of real-world large-scale ML models on state-of-the-art GPU clusters, we showcase potential throughput enhancements of up to 2.24x for pre-training and up to 5.27x for inference scenarios, respectively.

关键词： data Center Distributed Inference Distributed Training GPU Hardware-Software Co-Design Machine Learning Parallelization Performance Model Simulator

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QEVIS: Multi-Grained visualization of Distributed Query Execution

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第1期30卷 153-163页

作者： Shen, Qiaomu You, Zhengxin Yan, Xiao Zhang, Chaozu Xu, Ke Zeng, Dan Qin, Jianbin Tang, Bo Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Shenzhen Univ Shenzhen Inst Comp Sci Shenzhen Peoples R China

Distributed query processing systems such as Apache Hive and Spark are widely-used in many organizations for large-scale data analytics. Analyzing and understanding the query execution process of these systems are daily routines for engineers and crucial for identifying performance problems, optimizing system configurations, and rectifying errors. However, existing visualization tools for distributed query execution are insufficient because (i) most of them (if not all) do not provide fine-grained visualization (i.e., the atomic task level), which can be crucial for understanding query performance and reasoning about the underlying execution anomalies, and (ii) they do not support proper linkages between system status and query execution, which makes it difficult to identify the causes of execution problems. To tackle these limitations, we propose QEVIS, which visualizes distributed query execution process with multiple views that focus on different granularities and complement each other. Specifically, we first devise a query logical plan layout algorithm to visualize the overall query execution progress compactly and clearly. We then propose two novel scoring methods to summarize the anomaly degrees of the jobs and machines during query execution, and visualize the anomaly scores intuitively, which allow users to easily identify the components that are worth paying attention to. Moreover, we devise a scatter plot-based task view to show a massive number of atomic tasks, where task distribution patterns are informative for execution problems. We also equip QEVIS with a suite of auxiliary views and interaction methods to support easy and effective cross-view exploration, which makes it convenient to track the causes of execution problems. QEVIS has been used in the production environment of our industry partner, and we present three use cases from real-world applications and user interview to demonstrate its effectiveness. QEVIS is open-source at https://***/

关键词： visual analytics system distributed query execution performance analysis

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MGIMM: Multi-Granularity Instruction Multimodal Model for Attribute-Guided Remote Sensing Image Detailed Description

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ieee TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Yang, Cong Li, Zuchao Zhang, Lefei Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Sci Wuhan 430072 Peoples R China Hubei Luojia Lab Wuhan 430079 Peoples R China

Generating detailed textual descriptions of remote sensing images is challenging because it requires capturing both global and local visual information. The complexity of backgrounds and the scale variations among targets make it difficult to align visual regions with corresponding textual attributes. Furthermore, large multimodal models, while effective in general scenarios, struggle in remote sensing due to their lack of specialized knowledge and regional awareness. To address these issues, this article proposes an attribute-guided multi-granularity instruction multimodal model (MGIMM) for remote sensing image detailed description. MGIMM guides the multimodal model to learn the consistency between visual regions and corresponding text attributes (such as object names, colors, and shapes) through region-level instruction tuning. Then, with the multimodal model aligned on region attribute, guided by multigrain visual features, MGIMM fully perceives both region-level and global image information, utilizing large language models for comprehensive descriptions of remote sensing images. Due to the lack of a standard benchmark for generating detailed descriptions of remote sensing images, we construct a dataset featuring 38320 region-attribute pairs and 23463 image-detailed description pairs. Compared with various advanced methods on this dataset, the results demonstrate the effectiveness of MGIMM's region-attribute-guided learning approach.

关键词： Remote sensing visualization Tuning Sensors large language models Training Roads Object detection Image color analysis data models Instruction tuning multimodal information alignment remote sensing image detailed description

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Interactive visualization of Time-Varying Flow Fields Using Particle Tracing Neural Networks 17

Interactive Visualization of Time-Varying Flow Fields Using ...

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17th Pacific visualization Conference (PacificVis)

作者： Han, Mengjiao Li, Jixian Sane, Sudhanshu Gupta, Shubham Wang, Bei Petruzza, Steve Johnson, Chris R. Univ Utah SCI Inst Salt Lake City UT 84112 USA Utah State Univ Logan UT 84322 USA

ISBN: (纸本)9798350393811;9798350393804

Lagrangian representations of flow fields have gained prominence for enabling fast, accurate analysis and exploration of time-varying flow behaviors. In this paper, we present a comprehensive evaluation to establish a robust and efficient framework for Lagrangian-based particle tracing using deep neural networks (DNNs). Han et al. (2021) first proposed a DNN-based approach to learn Lagrangian representations and demonstrated accurate particle tracing for an analytic 2D flow field. In this paper, we extend and build upon this prior work in significant ways. First, we evaluate the performance of DNN models to accurately trace particles in various settings, including 2D and 3D time-varying flow fields, flow fields from multiple applications, flow fields with varying complexity, as well as structured and unstructured input data. Second, we conduct an empirical study to inform best practices with respect to particle tracing model architectures, activation functions, and training data structures. Third, we conduct a comparative evaluation of prior techniques that employ flow maps as input for exploratory flow visualization. Specifically, we compare our extended model against its predecessor by Han et al. (2021), as well as the conventional approach that uses triangulation and Barycentric coordinate interpolation. Finally, we consider the integration and adaptation of our particle tracing model with different viewers. We provide an interactive web-based visualization interface by leveraging the efficiencies of our framework, and perform high-fidelity interactive visualization by integrating it with an OSPRay-based viewer. Overall, our experiments demonstrate that using a trained DNN model to predict new particle trajectories requires a low memory footprint and results in rapid inference. Following best practices for large 3D datasets, our deep learning approach using GPUs for inference is shown to require approximately 46 times less memory while being more than 400 times f

关键词： Flow visualization Lagrangian-based particle tracing deep learning neural networks scientific machine learning

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