Summary of Vemoclap: a Video Emotion Classification Web Application, by Serkan Sulun et al.
VEMOCLAP: A video emotion classification web application
by Serkan Sulun, Paula Viana, Matthew E. P. Davies
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces VEMOCLAP, a web application that analyzes the emotional content of user-provided videos. The approach improves upon previous work by efficiently fusing pretrained features using multi-head cross-attention. This results in a 4.3% increase in state-of-the-art classification accuracy on the Ekman-6 video emotion dataset. The paper also provides an online application for users to run the model on their own videos or YouTube videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VEMOCLAP is a new tool that can analyze emotions in videos. It’s like a superpower that can understand how people feel when they watch a movie or a funny video. The creators of VEMOCLAP improved upon earlier technology by combining different features to better predict emotions. This means the app can now correctly identify emotions more accurately than before. Anyone can use VEMOCLAP to try it out on their own videos or favorite YouTube clips. |
Keywords
» Artificial intelligence » Classification » Cross attention
