SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models

Lee Hyun, Kim Sung-Bin, Seungju Han, Youngjae Yu, Tae-Hyun Oh


Abstract
Despite the recent advances in artificial intelligence, building social intelligence remains a challenge.Among social signals, laughter is one of the distinctive expressions that occurs during social interactions between humans.In this work, we tackle a new challenge for machines to understand the rationale behind laughter in video, Video Laugh Reasoning.We introduce this new task to explain why people laugh in a particular video and a dataset for this task.Our proposed dataset, SMILE, comprises video clips and language descriptions of why people laugh. We propose a baseline by leveraging the reasoning capacity of large language models (LLMs) with textual video representation. Experiments show that our baseline can generate plausible explanations for laughter. We further investigate the scalability of our baseline by probing other video understanding tasks and in-the-wild videos. We release our dataset, code, and model checkpoints on https://github.com/postech-ami/SMILE-Dataset.
Anthology ID:
2024.findings-naacl.73
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1149–1167
Language:
URL:
https://aclanthology.org/2024.findings-naacl.73
DOI:
Bibkey:
Cite (ACL):
Lee Hyun, Kim Sung-Bin, Seungju Han, Youngjae Yu, and Tae-Hyun Oh. 2024. SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1149–1167, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models (Hyun et al., Findings 2024)
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https://aclanthology.org/2024.findings-naacl.73.pdf
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