Image to Video Domain Adaptation Using Web Supervision

@article{Kae2020ImageTV,
  title={Image to Video Domain Adaptation Using Web Supervision},
  author={Andrew Kae and Yale Song},
  journal={2020 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2020},
  pages={556-564}
}
  • Andrew Kae, Yale Song
  • Published 2020
  • Computer Science
  • 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
  • Training deep neural networks typically requires large amounts of labeled data which may be scarce or expensive to obtain for a particular target domain. As an alternative, we can leverage webly-supervised data (i.e. results from a public search engine) which are relatively plentiful but may contain noisy results. In this work, we propose a novel two-stage approach to learn a video classifier using webly-supervised data. We argue that learning appearance features and temporal features… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 38 REFERENCES
    Deep Residual Learning for Image Recognition
    • 49,669
    • Highly Influential
    • Open Access
    Generative Adversarial Nets
    • 17,545
    • Highly Influential
    • Open Access
    Gradient-based learning applied to document recognition
    • 24,117
    • Open Access
    Neural Machine Translation by Jointly Learning to Align and Translate
    • 12,758
    • Open Access
    ImageNet: A large-scale hierarchical image database
    • 12,066
    • Highly Influential
    • Open Access
    Learning Spatiotemporal Features with 3D Convolutional Networks
    • 3,405
    • Open Access
    Visualizing Data using t-SNE
    • 13,568
    • Open Access
    Adversarial Discriminative Domain Adaptation
    • 1,482
    • Open Access