Summary of Localizing Moments Of Actions in Untrimmed Videos Of Infants with Autism Spectrum Disorder, by Halil Ismail Helvaci et al.
Localizing Moments of Actions in Untrimmed Videos of Infants with Autism Spectrum Disorder
by Halil Ismail Helvaci, Sen-ching Samson Cheung, Chen-Nee Chuah, Sally Ozonoff
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed machine learning-based Temporal Action Localization (TAL) model is designed to automate Autism Spectrum Disorder (ASD) screening in infant videos. The self-attention mechanism simplifies complex modeling and prioritizes efficiency, making it suitable for real-world deployment. This study focuses on developing computer vision methods that can operate effectively in naturilistic environments with minimal equipment control, addressing key challenges in ASD screening. By achieving 70% accuracy for look face, 79% for look object, 72% for smile, and 65% for vocalization, the TAL model shows promising results for early intervention and support. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer algorithms to help doctors identify Autism Spectrum Disorder (ASD) in babies. ASD can be hard to diagnose, so researchers want to make it easier by using machine learning techniques to look at videos of babies. They made a new kind of model that can find certain behaviors, like looking or smiling, and did tests to see how well it worked. The results were good, with the model able to spot these behaviors most of the time. |
Keywords
* Artificial intelligence * Machine learning * Self attention
