Summary of Perla: Perceptive 3d Language Assistant, by Guofeng Mei and Wei Lin and Luigi Riz and Yujiao Wu and Fabio Poiesi and Yiming Wang
PerLA: Perceptive 3D Language Assistant
by Guofeng Mei, Wei Lin, Luigi Riz, Yujiao Wu, Fabio Poiesi, Yiming Wang
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This research paper introduces PerLA, a 3D language assistant that enables large language models to better understand the physical world. Current point cloud processing strategies often downsample or divide scenes, risking the loss of local details and global context. PerLA addresses this challenge by capturing high-resolution details in parallel from different areas and integrating them with global context obtained from a lower-resolution whole point cloud. The paper presents a novel algorithm that preserves locality through the Hilbert curve and aggregates information via cross-attention and graph neural networks. A novel loss function is also introduced to promote training stability. PerLA outperforms state-of-the-art 3D language assistants, achieving gains of up to +1.34 CiDEr on ScanQA for question answering and +4.22 on ScanRefer and +3.88 on Nr3D for dense captioning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where computers can understand what they see in 3D space just like we do! This paper introduces PerLA, a new tool that helps large language models (computers) better comprehend the physical world. Right now, computer vision is limited because current methods either lose important details or ignore the bigger picture. PerLA solves this problem by taking both local details and global context into account when processing 3D data. The researchers also developed a new algorithm to make PerLA more efficient and stable. As a result, PerLA performs better than existing computer vision systems in tasks like answering questions about images and generating descriptions of scenes. |
Keywords
» Artificial intelligence » Cross attention » Loss function » Question answering
