Summary of Successes and Limitations Of Object-centric Models at Compositional Generalisation, by Milton L. Montero and Jeffrey S. Bowers and Gaurav Malhotra
Successes and Limitations of Object-centric Models at Compositional Generalisation
by Milton L. Montero, Jeffrey S. Bowers, Gaurav Malhotra
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 capabilities of disentangled latent variable models in compositional learning tasks. Despite being designed for factorizing datasets into constituent factors of variation, these models show limited compositional generalization skills. In contrast, object-centric architectures have shown promising compositional abilities, but their experiments have been limited to scene composition tasks. This work extends the compositional generalization skills of object-centric architectures to novel combinations of object properties and identifies the source of these skills. The authors also provide evidence on how these skills can be improved through careful training. However, they highlight an important limitation that still exists, suggesting new directions for research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computer models can learn from many examples. It finds that some models are better than others at putting together different parts to make something new. The authors look at these “object-centric” models and find that they’re good at combining objects in a scene, but not as good at combining different properties of those objects. They show that these models can be improved with the right training. This is important because it could help us build better AI systems. |
Keywords
* Artificial intelligence * Generalization
