The recently proposed audio-visual scene-aware dialog task paves the way to a more data-driven way of learning virtual assistants, smart speakers and car navigation systems. However, very little is known to date about how to effectively extract meaningful information from a plethora of sensors that pound the computational engine of those devices. Therefore, in this paper, we provide and carefully analyze a simple baseline for audio-visual scene-aware dialog which is trained end-to-end. Our method differentiates in a data-driven manner useful signals from distracting ones using an attention mechanism. We evaluate the proposed approach on the recently introduced and challenging audio-visual scene-aware dataset, and demonstrate the key features that permit to outperform the current state-of-the-art by more than 20\% on CIDEr.
@article{arxiv.1904.05876,
title = {A Simple Baseline for Audio-Visual Scene-Aware Dialog},
author = {Idan Schwartz and Alexander Schwing and Tamir Hazan},
journal= {arXiv preprint arXiv:1904.05876},
year = {2019}
}