Summary of Naturally Supervised 3d Visual Grounding with Language-regularized Concept Learners, by Chun Feng et al.
Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners
by Chun Feng, Joy Hsu, Weiyu Liu, Jiajun Wu
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 paper proposes the Language-Regularized Concept Learner (LARC), a neuro-symbolic concept learner that uses language constraints as regularization to improve accuracy in naturally supervised 3D visual grounding. LARC leverages large language models to distill constraints from language properties and regularize structured representations. The approach outperforms prior works in naturally supervised 3D visual grounding, demonstrating zero-shot composition, data efficiency, and transferability capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a new way to help machines understand 3D scenes without needing lots of labeled pictures. They created a special kind of computer program called the Language-Regularized Concept Learner (LARC). This program uses rules from language, like what words mean in relation to each other, to make its predictions better. The LARC program is important because it can help machines learn new things and make smart decisions without needing lots of labeled pictures. |
Keywords
» Artificial intelligence » Grounding » Regularization » Supervised » Transferability » Zero shot
