# Audio-Visual Scene-Aware Dialog

## TL;DR

This paper introduces the scene-aware dialog task, creating a new dataset and baseline models to generate natural responses grounded in video and audio content, advancing multimodal dialog systems.

## Contribution

It presents the AVSD dataset for scene-aware dialog and evaluates baseline models, highlighting the importance of integrating video, audio, and dialog history.

## Key findings

- Models need all inputs for optimal performance
- Baseline systems demonstrate the feasibility of the task
- The dataset enables future research in multimodal dialog

## 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.