Scalable Class-Centric Visual Interactive Labeling

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Joint Human-Machine Data Exploration
• Date: June 2025
• Article Number: 104240
• DOI: 10.1016/j.cag.2025.104240
• ISSN: 1873-7684
• Journal: COMPUTERS & GRAPHICS-UK
• Pages: 14
• Volume: 129
• Publisher: PERGAMON-ELSEVIER SCIENCE LTD
• Keywords: Class-centric labeling, Interactive machine learning, Property measures, Visual analytics, Visual-interactive data labeling

Abstract

Large unlabeled datasets demand efficient and scalable data labeling solutions, in particular when the number of instances and classes is large. This leads to significant visual scalability challenges and imposes a high cognitive load on the users. Traditional instance-centric labeling methods, where (single) instances are labeled in each iteration struggle to scale effectively in these scenarios. To address these challenges, we introduce cVIL, a Class-Centric Visual Interactive Labeling methodology designed for interactive visual data labeling. By shifting the paradigm from assigning-classes-to-instances to assigning-instances-to-classes, cVIL reduces labeling effort and enhances efficiency for annotators working with large, complex and class-rich datasets. We propose a novel visual analytics labeling interface built on top of the conceptual cVIL workflow, enabling improved scalability over traditional visual labeling. In a user study, we demonstrate that cVIL can improve labeling efficiency and user satisfaction over instance-centric interfaces. The effectiveness of cVIL is further demonstrated through a usage scenario, showcasing its potential to alleviate cognitive load and support experts in managing extensive labeling tasks efficiently.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1016/j.cag.2025.104240

BibTeX

@article{matt-2025-scv,
  title =      "Scalable Class-Centric Visual Interactive Labeling",
  author =     "Matthias Matt and Jana Sedlakova and J\"{u}rgen Bernard and
               Matthias Zeppelzauer and Manuela Waldner",
  year =       "2025",
  abstract =   "Large unlabeled datasets demand efficient and scalable data
               labeling solutions, in particular when the number of
               instances and classes is large. This leads to significant
               visual scalability challenges and imposes a high cognitive
               load on the users. Traditional instance-centric labeling
               methods, where (single) instances are labeled in each
               iteration struggle to scale effectively in these scenarios.
               To address these challenges, we introduce cVIL, a
               Class-Centric Visual Interactive Labeling methodology
               designed for interactive visual data labeling. By shifting
               the paradigm from assigning-classes-to-instances to
               assigning-instances-to-classes, cVIL reduces labeling effort
               and enhances efficiency for annotators working with large,
               complex and class-rich datasets. We propose a novel visual
               analytics labeling interface built on top of the conceptual
               cVIL workflow, enabling improved scalability over
               traditional visual labeling. In a user study, we demonstrate
               that cVIL can improve labeling efficiency and user
               satisfaction over instance-centric interfaces. The
               effectiveness of cVIL is further demonstrated through a
               usage scenario, showcasing its potential to alleviate
               cognitive load and support experts in managing extensive
               labeling tasks efficiently.",
  month =      jun,
  articleno =  "104240",
  doi =        "10.1016/j.cag.2025.104240",
  issn =       "1873-7684",
  journal =    "COMPUTERS & GRAPHICS-UK",
  pages =      "14",
  volume =     "129",
  publisher =  "PERGAMON-ELSEVIER SCIENCE LTD",
  keywords =   "Class-centric labeling, Interactive machine learning,
               Property measures, Visual analytics, Visual-interactive data
               labeling",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/matt-2025-scv/",
}