Papers by Tae-Hyun Oh
SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter (2026.acl-long)
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| Challenge: | Existing approaches to understanding laughter or humor focus on narrowly defined tasks such as detecting humor and estimating humor intensity. |
| Approach: | They propose a dataset for real-world laughter understanding with multimodal textual representations and question–answer annotations. |
| Outcome: | The proposed framework outperforms baselines in three laughter-related tasks, showing that it is robust. |
SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models (2024.findings-naacl)
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| Challenge: | Despite advances in artificial intelligence, building social intelligence remains a challenge. |
| Approach: | They propose a task to explain why people laugh in a video and a dataset to do this. |
| Outcome: | The proposed dataset generates plausible explanations for laughter in video and in-the-wild videos. |
Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach (D19-1)
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| Challenge: | Recent work on image captioning has made impressive progress . however, the results are limited and the model is difficult to train . |
| Approach: | They propose a semi-supervised framework for training an image captioning model by assigning pseudo-labels to unpaired samples via Generative Adversarial Networks. |
| Outcome: | The proposed framework is compared to baselines when the number of paired samples is scarce. |