Kilich/affective-visdial

<div align="center"> <p align="center"> <img src="assets/img/web_teaser.png" width=500px/> </p> <h1 align="center"> </h1> <h1 align="center"> Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations </h1>

  

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📰 News

• —30/08/2023: The preprint of our paper is now available on arXiv.

Summary

• —📰 News
• —Summary
• —📚 Introduction
• —📊 Baselines
• —Citation
• —References

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📚 Introduction

AffectVisDial is a large-scale dataset which consists of 50K 10-turn visually grounded dialogs as well as concluding emotion attributions and dialog-informed textual emotion explanations.

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📊 Baselines

We provide baseline models explanation generation task:

• —GenLM: BERT- and BART-based models [3, 4]
• —NLX-GPT: NLX-GPT based model [1]

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Citation

If you use our dataset, please cite the two following references:

@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, Li-Jia and Elsayed, Gamaleldin and Elhoseiny, Mohamed},
  journal={arXiv preprint arXiv:2308.16349},
  year={2023}
}

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References

1. 1._[Sammani et al., 2022] - NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks
2. 2.[Li et al., 2022] - BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
3. 3.[Lewis et al., 2019] - BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.
4. 4.[Dewlin et al., 2018] - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding