Name
Abstract | ||
|---|---|---|
Humor is a very important communication tool; yet, it is an open problem for machines to understand humor. In this paper, we build a new multimodal dataset for humor prediction that includes subtitles and video frames, as well as humor labels associated with video's timestamps. On top of it, we present a model to predict whether a subtitle causes laughter. Our model uses the visual modality through facial expression and character name recognition, together with the verbal modality, to explore how the visual modality helps. In addition, we use an attention mechanism to adjust the weight for each modality to facilitate humor prediction. Interestingly, our experimental results show that the performance boost by combinations of different modalities, and the attention mechanism and the model mostly relies on the verbal modality. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WACV48630.2021.00212 | 2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021 |
DocType | ISSN | Citations |
Conference | 2472-6737 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuta Kayatani | 1 | 0 | 0.34 |
| Zekun Yang | 2 | 8 | 4.56 |
| Mayu Otani | 3 | 39 | 8.40 |
| Noa García | 4 | 12 | 7.36 |
| Chenhui Chu | 5 | 60 | 23.45 |
| Nakashima Yuta | 6 | 1 | 1.36 |
| Haruo Takemura | 7 | 0 | 0.34 |