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Guanbin Li - IEEE Xplore Author Profile
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Guanbin Li

Also published under: Ge Li

Publications
126
Citations
5,030
Publications by Year
20112025
Co-Authors:
Show All Co-Authors (252)
Image of author Guanbin Li

Guanbin Li

Also published under: Ge Li

Affiliation

School of Computer Science and Engineering, Sun Yat-sen University, China
Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China

Publication Topics

Biography

Guanbin Li received the Ph.D. degree from The University of Hong Kong in 2016. He is currently an Associate Professor with the School of Data and Computer Science, Sun Yat-sen University. His current research interests include computer vision, image processing, and deep learning. He has authorized and coauthorized on more than 50 papers in top-tier academic journals and conferences. He was a recipient of the ICCV 2019 Best Paper Nomination Award. He serves as the Area Chair for the conference of VISAPP. He has been serving as a Reviewer for numerous academic journals and conferences, such as IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Image Processing (TIP), IEEE Transactions on Multimedia (T...
Publications
126
Citations
5,030
Publications by Year
20112025
Co-Authors:
Show All Co-Authors (252)
Author's Published Works

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TimePool: Visually Answer “Which and When” Questions On Univariate Time Series

Tinghao Feng;Yueqi Hu;Jing Yang;Tom Polk;Ye Zhao;Shixia Liu;Zhaocong Yang

2023 IEEE Visualization and Visual Analytics (VIS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
When exploring time series datasets, analysts often pose “which and when” questions. For example, with world life expectancy data over one hundred years, they may inquire about the top 10 countries in life expectancy and the time period when they achieved this status, or which countries have had longer life expectancy than Ireland and when. This paper proposes TimePool, a new visualization prototy...Show More

TimePool: Visually Answer “Which and When” Questions On Univariate Time Series

Tinghao Feng;Yueqi Hu;Jing Yang;Tom Polk;Ye Zhao;Shixia Liu;Zhaocong Yang

Year: 2023 | Conference Paper |

Investigating the Sketchplan: A Novel Way of Identifying Tactical Behavior in Massive Soccer Datasets

Daniel Seebacher;Tom Polk;Halldor Janetzko;Daniel A. Keim;Tobias Schreck;Manuel Stein

IEEE Transactions on Visualization and Computer Graphics
Year: 2023 | Volume: 29, Issue: 4 | Journal Article |
Cited by: Papers (9)
Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards; or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interacti...Show More

Investigating the Sketchplan: A Novel Way of Identifying Tactical Behavior in Massive Soccer Datasets

Daniel Seebacher;Tom Polk;Halldor Janetzko;Daniel A. Keim;Tobias Schreck;Manuel Stein

Year: 2023 | Volume: 29, Issue: 4 | Journal Article |

CourtTime: Generating Actionable Insights into Tennis Matches Using Visual Analytics

Tom Polk;Dominik Jäckle;Johannes Häußler;Jing Yang

IEEE Transactions on Visualization and Computer Graphics
Year: 2020 | Volume: 26, Issue: 1 | Journal Article |
Cited by: Papers (21)
Tennis players and coaches of all proficiency levels seek to understand and improve their play. Summary statistics alone are inadequate to provide the insights players need to improve their games. Spatio-temporal data capturing player and ball movements is likely to provide the actionable insights needed to identify player strengths, weaknesses, and strategies. To fully utilize this spatio-tempora...Show More

CourtTime: Generating Actionable Insights into Tennis Matches Using Visual Analytics

Tom Polk;Dominik Jäckle;Johannes Häußler;Jing Yang

Year: 2020 | Volume: 26, Issue: 1 | Journal Article |

FDive: Learning Relevance Models Using Pattern-based Similarity Measures

Frederik L. Dennig;Tom Polk;Zudi Lin;Tobias Schreck;Hanspeter Pfister;Michael Behrisch

2019 IEEE Conference on Visual Analytics Science and Technology (VAST)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity...Show More

FDive: Learning Relevance Models Using Pattern-based Similarity Measures

Frederik L. Dennig;Tom Polk;Zudi Lin;Tobias Schreck;Hanspeter Pfister;Michael Behrisch

Year: 2019 | Conference Paper |

Visual Analytics of Volunteered Geographic Information: Detection and Investigation of Urban Heat Islands

Daniel Seebacher;Matthias Miller;Tom Polk;Johannes Fuchs;Daniel A. Keim

IEEE Computer Graphics and Applications
Year: 2019 | Volume: 39, Issue: 5 | Magazine Article |
Cited by: Papers (5)
Urban heat islands are local areas where the temperature is much higher than in the vicinity and are a modern phenomenon that occurs mainly in highly developed areas, such as large cities. This effect has a negative impact on energy management in buildings, and also has a direct impact on human health, especially for elderly people. With the advent of volunteered geographic information from privat...Show More

Visual Analytics of Volunteered Geographic Information: Detection and Investigation of Urban Heat Islands

Daniel Seebacher;Matthias Miller;Tom Polk;Johannes Fuchs;Daniel A. Keim

Year: 2019 | Volume: 39, Issue: 5 | Magazine Article |

Spot-tracking lens: A zoomable user interface for animated bubble charts

Yueqi Hu;Tom Polk;Jing Yang;Ye Zhao;Shixia Liu

2016 IEEE Pacific Visualization Symposium (PacificVis)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
Zoomable user interfaces are widely used in static visualizations and have many benefits. However, they are not well supported in animated visualizations due to problems such as change blindness and information overload. We propose the spot-tracking lens, a new zoomable user interface for animated bubble charts, to tackle these problems. It couples zooming with automatic panning and provides a ric...Show More

Spot-tracking lens: A zoomable user interface for animated bubble charts

Yueqi Hu;Tom Polk;Jing Yang;Ye Zhao;Shixia Liu

Year: 2016 | Conference Paper |

TenniVis: Visualization for Tennis Match Analysis

Tom Polk;Jing Yang;Yueqi Hu;Ye Zhao

IEEE Transactions on Visualization and Computer Graphics
Year: 2014 | Volume: 20, Issue: 12 | Journal Article |
Cited by: Papers (69)
Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel...Show More

TenniVis: Visualization for Tennis Match Analysis

Tom Polk;Jing Yang;Yueqi Hu;Ye Zhao

Year: 2014 | Volume: 20, Issue: 12 | Journal Article |

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