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Weakly Supervised Learning of Object Segmentations from Web-Scale Videos

• G. Hartmann, M. Grundmann, J. Hoffman, D. Tsai, V. Kwatra, O. Madani, S. Vijayanarasimhan, I. Essa, J. Rehg, and R. Sukthankar (2012), “Weakly Supervised Learning of Object Segmentations from Web-Scale Videos,” in Proceedings of ECCV 2012 Workshop on Web-scale Vision and Social Media, 2012. [PDF] [DOI] [BIBTEX]

  @InProceedings{    2012-Hartmann-WSLOSFWV,
    author  = {Glenn Hartmann and Matthias Grundmann and Judy
        Hoffman and David Tsai and Vivek Kwatra and Omid
        Madani and Sudheendra Vijayanarasimhan and Irfan
        Essa and James Rehg and Rahul Sukthankar},
    booktitle  = {Proceedings of ECCV 2012 Workshop on Web-scale
        Vision and Social Media},
    doi    = {10.1007/978-3-642-33863-2_20},
    pdf    = {http://www.cs.cmu.edu/~rahuls/pub/eccv2012wk-cp-rahuls.pdf},
    title    = {Weakly Supervised Learning of Object Segmentations
        from Web-Scale Videos},
    year    = {2012}
  }

Abstract

We propose to learn pixel-level segmentations of objects from weakly labeled (tagged) internet videos. Specically, 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 classiers for a set of independent spatio-temporal segments. The object seeds obtained using segment-level classiers are further rened 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 conrm that our proposed methods can learn good object masks just by watching YouTube.

Presented at: ECCV 2012 Workshop on Web-scale Vision and Social Media, 2012, October 7-12, 2012, in Florence, ITALY.

Awarded the BEST PAPER AWARD!

Tags: 2012, Activity Recognition, Awards, Computational Video, Computer Vision, Publications | Categories: Activity Recognition, Awards, Google, Matthias Grundmann, Multimedia, PAMI/ICCV/CVPR/ECCV, Papers, Vivek Kwatra, WWW | Date: October 7th, 2012 | By: Irfan Essa |

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