Summary of Minigpt-3d: Efficiently Aligning 3d Point Clouds with Large Language Models Using 2d Priors, by Yuan Tang et al.
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
by Yuan Tang, Xu Han, Xianzhi Li, Qiao Yu, Yixue Hao, Long Hu, Min Chen
First submitted to arxiv on: 2 May 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 introduces MiniGPT-3D, a large 3D point cloud-language model that achieves state-of-the-art (SOTA) results in 3D object classification and captioning tasks while training for only 27 hours on one RTX 3090. To achieve this efficiency, the authors propose a novel four-stage training strategy for modality alignment and a mixture of query experts module to adaptively aggregate features. They also utilize parameter-efficient fine-tuning methods LoRA and Norm fine-tuning, resulting in only 47.8M learnable parameters, which is up to 260x fewer than existing methods. The authors demonstrate the effectiveness of MiniGPT-3D by comparing its performance with ShapeLLM-13B, which costs 160 total GPU-hours on 8 A800. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new kind of language model that can understand and work with three-dimensional point clouds. This is important because it allows computers to better understand and interact with the world around us. The authors create a more efficient way to train these models, which makes them more practical for use in real-world applications. |
Keywords
» Artificial intelligence » Alignment » Classification » Fine tuning » Language model » Lora » Parameter efficient
