Matthias Matt, Matthias Zeppelzauer, Manuela WaldnerORCID iD
cVIL: Class-Centric Visual Interactive Labeling
In Eurographics Proceedings. May 2024.
[paper]

Information

  • Publication Type: Conference Paper
  • Workgroup(s)/Project(s):
  • Date: May 2024
  • ISBN: 978-3-03868-056-7
  • Publisher: Eurographics
  • Open Access: yes
  • Location: Aarhus
  • Lecturer: Matthias Matt
  • Event: EuroVis Workshop on Visual Analytics (EuroVA 2024)
  • Editor: El-Assady, Mennatallah and Schulz, Hans-Jorg
  • DOI: 10.2312/eurova.20241113
  • Booktitle: Eurographics Proceedings
  • Pages: 6
  • Conference date: 27. May 2024
  • Keywords: Visual Analytics, Interactive Machine Learning, User Interface Design

Abstract

We present cVIL, a class-centric approach to visual interactive labeling, which facilitates human annotation of large and complex image data sets. cVIL uses different property measures to support instance labeling for labeling difficult instances and batch labeling to quickly label easy instances. Simulated experiments reveal that cVIL with batch labeling can outperform traditional labeling approaches based on active learning. In a user study, cVIL led to better accuracy and higher user preference compared to a traditional instance-based visual interactive labeling approach based on 2D scatterplots.

Additional Files and Images

Additional images and videos

cVIL teaser: Screenshot of cVIL as employed in the user study cVIL teaser: Screenshot of cVIL as employed in the user study

Additional files

Weblinks

BibTeX

@inproceedings{matt-2024-cvil,
  title =      "cVIL: Class-Centric Visual Interactive Labeling",
  author =     "Matthias Matt and Matthias Zeppelzauer and Manuela Waldner",
  year =       "2024",
  abstract =   "We present cVIL, a class-centric approach to visual
               interactive labeling, which facilitates human annotation of
               large and complex image data sets. cVIL uses different
               property measures to support instance labeling for labeling
               difficult instances and batch labeling to quickly label easy
               instances. Simulated experiments reveal that cVIL with batch
               labeling can outperform traditional labeling approaches
               based on active learning. In a user study, cVIL led to
               better accuracy and higher user preference compared to a
               traditional instance-based visual interactive labeling
               approach based on 2D scatterplots.",
  month =      may,
  isbn =       "978-3-03868-056-7",
  publisher =  "Eurographics",
  location =   "Aarhus",
  event =      "EuroVis Workshop on Visual Analytics (EuroVA 2024)",
  editor =     "El-Assady, Mennatallah and Schulz, Hans-Jorg",
  doi =        "10.2312/eurova.20241113",
  booktitle =  "Eurographics Proceedings",
  pages =      "6",
  keywords =   "Visual Analytics, Interactive Machine Learning, User
               Interface Design",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/matt-2024-cvil/",
}