Summary of Alps: An Auto-labeling and Pre-training Scheme For Remote Sensing Segmentation with Segment Anything Model, by Song Zhang et al.
ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model
by Song Zhang, Qingzhong Wang, Junyi Liu, Haoyi Xiong
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed ALPS framework leverages the Segment Anything Model (SAM) to predict pseudo-labels for Remote Sensing (RS) images without requiring prior annotations or additional prompts. The pipeline significantly reduces labor and resource demands traditionally associated with annotating RS datasets, enhancing performance in downstream tasks across various benchmarks, including iSAID and ISPRS Potsdam. Experiments demonstrate the effectiveness of ALPS, showcasing its ability to generalize well across multiple tasks even under the scarcity of extensively annotated datasets, offering a scalable solution to automatic segmentation and annotation challenges in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ALPS is a new way to help machines understand pictures taken from space or medical images without needing people to label them. The method uses an existing model called SAM to guess what’s in the picture, making it much faster and cheaper than before. This helps make computers better at tasks like finding buildings or organs in images. |
Keywords
» Artificial intelligence » Sam
