task_categories:text-classification task_categories:text2text-generation size_categories:10K<n<100K language:en license:apache-2.0 code arxiv:2308.16349 region:us
---
license: apache-2.0
task_categories:
- text-classification
- text2text-generation
language:
- en
tags:
- code
size_categories:
- 10K<n<100K
---

 Dataset Card for "Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations"

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)

## Dataset Description

- **Homepage:** [affective-visual-dialog.github.io/](https://affective-visual-dialog.github.io/)
- **Repository:** [More Information Needed](https://github.com/Vision-CAIR/affectiveVisDial)
- **Paper:** [More Information Needed](https://arxiv.org/abs/2308.16349)
- **Point of Contact:** [More Information Needed](kilichbek.haydarov@gmail.com)

### Dataset Summary

Affective Visual Dialog is an emotion explanation and reasoning task as a testbed for research on understanding the formation of emotions in visually grounded conversations. The task involves three skills: (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction and (3) Affective emotion explanation generation based on the dialog. Our key contribution is the collection of a large-scale dataset, dubbed AffectVisDial, consisting of 50K 10-turn visually grounded dialogs as well as concluding emotion attributions and dialog-informed textual emotion explanations, resulting in a total of 27,180 working hours. We explain our design decisions in collecting the dataset and introduce the questioner and answerer tasks that are associated with the participants in the conversation. We train and demonstrate solid Affective Visual Dialog baselines adapted from state-of-the-art models. Remarkably, the responses generated by our models show promising emotional reasoning abilities in response to visually grounded conversations.

### Supported Tasks and Leaderboards

We have one task that is available as a challenge:
- [Emotion Explanation Prediction](https://eval.ai/web/challenges/challenge-page/2151/overview)

Challenge have a leaderboard on Eval.ai. Submission deadlines can be viewed from the above links.

In addition, we are hosting the challenge at the ICCV23 workshop [5CLVL]((https://iccv-clvl.github.io/2023/)). We have cash prizes for winners.


### Citation Information

```
@article{haydarov2023affective,
title={Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations},
author={Haydarov, Kilichbek and Shen, Xiaoqian and Madasu, Avinash and Salem, Mahmoud and Li, Jia and Elsayed, Gamaleldin and Elhoseiny, Mohamed},
journal={arXiv preprint arXiv:2308.16349},
year={2023}
}
```