VEMOCLAP: A video emotion classification web application
Serkan Sulun, Paula Viana, Matthew E. P. Davies
Abstract
We introduce VEMOCLAP: Video EMOtion Classifier using Pretrained features, the first readily available and open-source web application that analyzes the emotional content of any user-provided video. We improve our previous work, which exploits open-source pretrained models that work on video frames and audio, and then efficiently fuse the resulting pretrained features using multi-head cross-attention. Our approach increases the state-of-the-art classification accuracy on the Ekman-6 video emotion dataset by 4.3% and offers an online application for users to run our model on their own videos or YouTube videos. We invite the readers to try our application at serkansulun.com/app.
Results
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Emotion Recognition | Ekman6 | Accuracy | 65.28 | VEMOCLAP |
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