Summary of Transfer Learning with Point Transformers, by Kartik Gupta and Rahul Vippala and Sahima Srivastava
Transfer Learning with Point Transformers
by Kartik Gupta, Rahul Vippala, Sahima Srivastava
First submitted to arxiv on: 1 Apr 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 This paper investigates the performance of Point Transformers for classification tasks on point cloud data. The attention-based mechanism in these models enables them to capture long-range spatial dependencies between multiple point sets. We evaluate the classification capabilities of these networks on the ModelNet10 dataset and, subsequently, fine-tune the trained model on 3D MNIST dataset. Additionally, we compare the performance of fine-tuned and from-scratch models on MNIST dataset. Surprisingly, transfer learned models do not outperform from-scratch models in this case due to significant differences between distributions in the two datasets. Although transfer learning may facilitate faster convergence by leveraging knowledge about lower-level features (edges, corners, etc.) from ModelNet10 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well machines can classify 3D objects using Point Transformers. These models are great for tasks like object detection and segmentation on point cloud data. The team tested the model’s ability to classify objects on two different datasets, ModelNet10 and 3D MNIST. They found that even though the trained model performed well on one dataset, it didn’t do better than starting from scratch on the other dataset because the distributions of the data were very different. |
Keywords
* Artificial intelligence * Attention * Classification * Object detection * Transfer learning
