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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] [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},
    Date-Added = {2012-10-23 15:03:18 +0000},
    Date-Modified = {2012-10-23 15:07:04 +0000},
    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.

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Tags: 2012, Activity Recognition, Awards, Computational Video, Computer Vision, Publications

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