Summary of Bayesian Inverse Graphics For Few-shot Concept Learning, by Octavio Arriaga et al.
Bayesian Inverse Graphics for Few-Shot Concept Learning
by Octavio Arriaga, Jichen Guo, Rebecca Adam, Sebastian Houben, Frank Kirchner
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 presents a Bayesian model of perception that learns from minimal data, enabling it to generalize new concepts from a single example. The proposed generative inverse graphics model infers posterior distributions over physically consistent parameters from one or several images. This representation is used for downstream tasks such as few-shot classification and pose estimation. The model outperforms existing few-shot neural-only classification algorithms and demonstrates generalization across varying lighting conditions, backgrounds, and out-of-distribution shapes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how humans can learn new concepts from just one example. It presents a special kind of computer vision model that requires very little data to work well. This model uses Bayes’ rule to figure out what an object looks like based on a single image or a few images. The model is good at predicting what an object will look like in different lighting conditions and backgrounds, even if the object is not exactly like ones it has seen before. |
Keywords
» Artificial intelligence » Classification » Few shot » Generalization » Pose estimation
