We present a method to improve video description generation by modeling higher-order interactions between video frames and described concepts. By storing past visual attention in the video associated to previously generated words, the system is able to decide what to look at and describe in light of what it has already looked at and described. This enables not only more effective local attention, but tractable consideration of the video sequence while generating each word. Evaluation on the challenging and popular MSVD and Charades datasets demonstrates that the proposed architecture outperforms previous video description approaches without requiring external temporal video features.
@article{arxiv.1611.02261,
title = {Memory-augmented Attention Modelling for Videos},
author = {Rasool Fakoor and Abdel-rahman Mohamed and Margaret Mitchell and Sing Bing Kang and Pushmeet Kohli},
journal= {arXiv preprint arXiv:1611.02261},
year = {2017}
}
Comments
Revised version, minor changes, add the link for the source codes