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<!DOCTYPE html>
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<title>MIntRec</title>
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<h3 style="font-weight: 600; line-height: 1.3">MIntRec: Multimodal Intent Recognition </h3>
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<p style='margin: 0 0 10px; color:#595959; font-weight: 400;font:18px/1.5 -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Fira Sans", "Droid Sans", "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";'>
A New Dataset for Multimodal Intent Recognition</p>
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<a style="color: #39c; " href="https://github.com/thuiar/MIntRec">View the Project on GitHub</a>
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<strong>Github </strong>
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Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing
intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets
with only text information.
It formulates coarse-grained and fine-grained intent taxonomies
based on the data collected from the TV series Superstore. The
dataset consists of 2,224 high-quality samples with text, video, and
audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes
of speakers in each video segment and achieve an automatic process
for speaker annotation. MIntRec is helpful for researchers to mine
relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality
and model cross-modal interactions by adapting three powerful
multimodal fusion methods to build baselines. The gap between the best-performing methods and humans indicates the challenge and importance of this task
for the community.
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<br>
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<br>
<strong>Statistics of the MIntRec dataset</strong>
<br>
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<td>Total number of coarse-grained intents</td>
<td>2</td>
</tr>
<tr>
<td>Total number of fine-grained intents</td>
<td>20</td>
</tr>
<tr>
<td>Total number of videos</td>
<td>43</td>
</tr>
<tr>
<td>Total number of video segments</td>
<td>2,224</td>
</tr>
<tr>
<td>Total number of words in text utterances</td>
<td> 15,658</td>
</tr>
<tr>
<td>Total number of unique words in text utterances</td>
<td> 2,562</td>
</tr>
<tr>
<td>Average length of text utterances</td>
<td> 7.04</td>
</tr>
<tr>
<td>Maximum length of text utterances</td>
<td> 26</td>
</tr>
<tr>
<td>Average length of video segments (s)</td>
<td>2.38</td>
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<td>Maximum length of video segments (s)</td>
<td>9.59</td>
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<br>
<br>
<h4 style="font-weight: 600; line-height: 1.3">
Intent taxonomies of our MIntRec dataset with brief interpretations.
</h4>
<br>
<img src="static/images/table.png" width=100%>
<br>
<br>
<h4 style="font-weight: 600; line-height: 1.3">
Voting statistics of 2,224 samples in MIntRec.
</h4>
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<img src="static/images/line.png" width=100%>
<br>
<br>
<br>
<h4 style="font-weight: 600; line-height: 1.3">Citation </h4>
<br>
<p style='text-align:justify;margin: 0 0 10px; color:#595959; font-weight: 400;font:18px/1.5 -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Fira Sans", "Droid Sans", "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";'>
Please cite the following papers if you use this dataset in your work.
</p>
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<pre id='my_pre'>
@inproceedings{10.1145/3503161.3547906,
author = {Zhang, Hanlei and Xu, Hua and Wang, Xin and Zhou, Qianrui and Zhao, Shaojie and Teng, Jiayan},
title = {MIntRec: A New Dataset for Multimodal Intent Recognition},
year = {2022},
doi = {10.1145/3503161.3547906},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
pages = {1688–1697},
numpages = {10}
}
</pre>
</div>
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