Summary of Self-supervised Learning Of Video Representations From a Child’s Perspective, by A. Emin Orhan et al.
Self-supervised learning of video representations from a child’s perspective
by A. Emin Orhan, Wentao Wang, Alex N. Wang, Mengye Ren, Brenden M. Lake
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
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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 abstract proposes exploring whether children can develop powerful internal models of the world through egocentric visual experiences using generic learning algorithms or strong inductive biases. To tackle this question, researchers have collected large-scale, longitudinal video datasets and trained self-supervised video models on headcam recordings from a child over two years (6-31 months). The results show that these models can effectively learn action concepts from labeled examples, scale well with data size, and even display emergent video interpolation capabilities. Additionally, the video models learned more accurate and robust object representations compared to image-based models trained on the same data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Children’s internal models of the world are powerful tools they develop through their visual experiences. But can these models be learned using simple learning algorithms or do they need special help? Scientists have collected lots of videos of a child from birth to age 3 and used those videos to train computers to learn new things without being taught directly. The results show that these computers can pick up on important actions and objects, even when shown only a few examples. This is exciting because it could help us understand how children develop their own internal models. |
Keywords
* Artificial intelligence * Self supervised
