Summary of Towards Child-inclusive Clinical Video Understanding For Autism Spectrum Disorder, by Aditya Kommineni et al.
Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder
by Aditya Kommineni, Digbalay Bose, Tiantian Feng, So Hyun Kim, Helen Tager-Flusberg, Somer Bishop, Catherine Lord, Sudarsana Kadiri, Shrikanth Narayanan
First submitted to arxiv on: 20 Sep 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 This paper investigates the use of foundation models across three modalities – speech, video, and text – to analyze child-focused interaction sessions for children with Autism Spectrum Disorder. The authors propose a unified methodology to combine multiple modalities by using large language models as reasoning agents. They evaluate their performance on two tasks: activity recognition and abnormal behavior detection. The multimodal pipeline shows robustness to modality-specific limitations and improves performance on clinical video analysis compared to unimodal settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors and researchers understand children with Autism Spectrum Disorder better by using computers to analyze videos of kids interacting with caregivers or professionals. Right now, people have to watch these long videos and write down what they see – which is time-consuming and requires special knowledge. The authors want to use powerful computer models that can learn from big datasets to help analyze these interactions. They test their approach on two tasks: recognizing what’s happening in the video (like “the child is playing”) and detecting unusual behavior. By combining different types of data, like speech, text, and video, they show that computers can do a better job than just looking at one type of data. |
Keywords
* Artificial intelligence * Activity recognition
