Audio Visual Scene-Aware Dialog

Huda Alamri, Vincent Cartillier, Abhishek Das, Jue Wang, Anoop Cherian, Irfan Essa, Dhruv Batra, Tim K. Marks, Chiori Hori, Peter Anderson, Stefan Lee, Devi Parikh; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7558-7567

Abstract


We introduce the task of scene-aware dialog. Our goal is to generate a complete and natural response to a question about a scene, given video and audio of the scene and the history of previous turns in the dialog. To answer successfully, agents must ground concepts from the question in the video while leveraging contextual cues from the dialog history. To benchmark this task, we introduce the Audio Visual Scene-Aware Dialog (AVSD) Dataset. For each of more than 11,000 videos of human actions from the Charades dataset, our dataset contains a dialog about the video, plus a final summary of the video by one of the dialog participants. We train several baseline systems for this task and evaluate the performance of the trained models using both qualitative and quantitative metrics. Our results indicate that models must utilize all the available inputs (video, audio, question, and dialog history) to perform best on this dataset.

Related Material


[pdf]
[bibtex]
@InProceedings{Alamri_2019_CVPR,
author = {Alamri, Huda and Cartillier, Vincent and Das, Abhishek and Wang, Jue and Cherian, Anoop and Essa, Irfan and Batra, Dhruv and Marks, Tim K. and Hori, Chiori and Anderson, Peter and Lee, Stefan and Parikh, Devi},
title = {Audio Visual Scene-Aware Dialog},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}