Weakly Supervised Learning of Object Segmentations from Web-Scale Video

  • Glenn Hartmann
  • Matthias Grundmann
  • Judy Hoffman
  • David Tsai
  • Vivek Kwatra
  • Omid Madani
  • Sudheendra Vijayanarasimhan
  • Irfan Essa
  • James Rehg
  • Rahul Sukthankar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)

Abstract

We propose to learn pixel-level segmentations of objects from weakly labeled (tagged) internet videos. Specifically, given a large collection of raw YouTube content, along with potentially noisy tags, our goal is to automatically generate spatiotemporal masks for each object, such as “dog”, without employing any pre-trained object detectors. We formulate this problem as learning weakly supervised classifiers for a set of independent spatio-temporal segments. The object seeds obtained using segment-level classifiers are further refined using graphcuts to generate high-precision object masks. Our results, obtained by training on a dataset of 20,000 YouTube videos weakly tagged into 15 classes, demonstrate automatic extraction of pixel-level object masks. Evaluated against a ground-truthed subset of 50,000 frames with pixel-level annotations, we confirm that our proposed methods can learn good object masks just by watching YouTube.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Glenn Hartmann
    • 1
  • Matthias Grundmann
    • 2
  • Judy Hoffman
    • 3
  • David Tsai
    • 2
  • Vivek Kwatra
    • 1
  • Omid Madani
    • 1
  • Sudheendra Vijayanarasimhan
    • 1
  • Irfan Essa
    • 2
  • James Rehg
    • 2
  • Rahul Sukthankar
    • 1
  1. 1.Google ResearchUSA
  2. 2.Georgia Institute of TechnologyUSA
  3. 3.University of CaliforniaBerkeleyUSA

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