Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation?
Name
CHI18-ensembles.pdf
Description
Accepted version
Size
3.17 MB
Format
Adobe PDF
Checksum (MD5)
b84b5b0473bb0215d33e75f18ade90d8
Author(s)
Kaspar, Alexandre
Patterson, Genevieve
Kim, Changil
Aksoy, Yagiz
Matusik, Wojciech
Elgharib, Mohamed
Alternative Title
How Can We Choreograph Crowd Workers for Video Segmentation?
Date Issued
April 19, 2018
Publisher
ACM
Citation
Kaspar, Alexandre, Patterson, Genevieve, Kim, Changil, Aksoy, Yagiz, Matusik, Wojciech et al. 2018. "Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation?."
Version
Author's final manuscript
Abstract
© 2018 Copyright is held by the owner/author(s). In this work, we propose two ensemble methods leveraging a crowd workforce to improve video annotation, with a focus on video object segmentation. Their shared principle is that while individual candidate results may likely be insufficient, they often complement each other so that they can be combined into something better than any of the individual results-The very spirit of collaborative working. For one, we extend a standard polygon-drawing interface to allow workers to annotate negative space, and combine the work of multiple workers instead of relying on a single best one as commonly done in crowdsourced image segmentation. For the other, we present a method to combine multiple automatic propagation algorithms with the help of the crowd. Such combination requires an understanding of where the algorithms fail, which we gather using a novel coarse scribble video annotation task. We evaluate our ensemble methods, discuss our design choices for them, and make our web-based crowdsourcing tools and results publicly available.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3173574.3173685
