Adaptive Feature Abstraction for Translating Video to Language

Yunchen Pu, Martin Renqiang Min, Zhe Gan, Lawrence Carin

Feb 14, 2017 (modified: Mar 17, 2017) ICLR 2017 workshop submission readers: everyone
  • Abstract: A new model for video captioning is developed, using a deep three-dimensional Convolutional Neural Network (C3D) as an encoder for videos and a Recurrent Neural Network (RNN) as a decoder for captions. A novel attention mechanism with spatiotemporal alignment is employed to adaptively and sequentially focus on different layers of CNN features (levels of feature "abstraction"), as well as local spatiotemporal regions of the feature maps at each layer. The proposed approach is evaluated on the YouTube2Text benchmark. Experimental results demonstrate quantitatively the effectiveness of our proposed adaptive spatiotemporal feature abstraction for translating videos to sentences with rich semantic structures.
  • Conflicts: duke.edu, nec-labs.com, virginia.edu

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