Translating Videos to Natural Language Using Deep Recurrent Neural Networks

Abstract

Solving the visual symbol grounding problem has long been a goal of artificial intelligence. The field appears to be advancing closer to this goal with recent breakthroughs in deep learning for natural language grounding in static images. In this paper, we propose to translate videos directly to sentences using a unified deep neural network with both… (More)
View Slides

Topics

9 Figures and Tables

Statistics

0501002015201620172018
Citations per Year

279 Citations

Semantic Scholar estimates that this publication has 279 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Venugopalan2015TranslatingVT, title={Translating Videos to Natural Language Using Deep Recurrent Neural Networks}, author={Subhashini Venugopalan and Huijuan Xu and Jeff Donahue and Marcus Rohrbach and Raymond J. Mooney and Kate Saenko}, booktitle={HLT-NAACL}, year={2015} }