Adaptive feature abstraction for translating video to language

Published

Conference Paper

© 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. All Rights Reserved. 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.

Duke Authors

Cited Authors

  • Pu, Y; Gan, Z; Carin, L; Min, MR

Published Date

  • January 1, 2019

Published In

  • 5th International Conference on Learning Representations, Iclr 2017 Workshop Track Proceedings

Citation Source

  • Scopus