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From Traditional to Modern: Domain Adaptation for Action Classification in Short Social Video Clips

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Pattern Recognition (GCPR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9796))

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Abstract

Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilize semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlabled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilize a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance.

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Notes

  1. 1.

    https://lear.inrialpes.fr/people/wang/improved_trajectories.

  2. 2.

    https://code.google.com/p/word2vec/.

  3. 3.

    https://github.com/mganjoo/zslearning.

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Authors and Affiliations

  1. Center for Visual Information Technology, IIIT Hyderabad, Hyderabad, India

    Aditya Singh, Saurabh Saini, Rajvi Shah & P. J. Narayanan

Authors
  1. Aditya Singh
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  2. Saurabh Saini
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  3. Rajvi Shah
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  4. P. J. Narayanan
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Correspondence to Aditya Singh .

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Editors and Affiliations

  1. University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Bjoern Andres

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Singh, A., Saini, S., Shah, R., Narayanan, P.J. (2016). From Traditional to Modern: Domain Adaptation for Action Classification in Short Social Video Clips. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_20

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_20

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