Summary of Adaptive Point Transformer, by Alessandro Baiocchi et al.
Adaptive Point Transformer
by Alessandro Baiocchi, Indro Spinelli, Alessandro Nicolosi, Simone Scardapane
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 a novel deep learning model, Adaptive Point Cloud Transformer (AdaPT), designed to efficiently process large point clouds while achieving competitive accuracy in classification tasks. By augmenting traditional point cloud transformers with an adaptive token selection mechanism and budget mechanism, AdaPT dynamically reduces the number of tokens during inference, addressing the scalability challenge posed by quadratic scaling. Experimental evaluation on point cloud classification tasks demonstrates significant reductions in computational complexity without sacrificing accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to use computers to process 3D data. This kind of data is used for things like virtual reality and self-driving cars. The new method, called Adaptive Point Cloud Transformer (AdaPT), helps computers deal with really big datasets by using less computer power when needed. It still gets good results on tasks like recognizing shapes in the data. This makes it useful for real-world applications where speed and efficiency matter. |
Keywords
* Artificial intelligence * Classification * Deep learning * Inference * Token * Transformer
