Summary of Mmearth: Exploring Multi-modal Pretext Tasks For Geospatial Representation Learning, by Vishal Nedungadi et al.
MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning
by Vishal Nedungadi, Ankit Kariryaa, Stefan Oehmcke, Serge Belongie, Christian Igel, Nico Lang
First submitted to arxiv on: 4 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 presents a novel approach for learning general-purpose representations from optical satellite images using a Multi-Pretext Masked Autoencoder (MP-MAE) model, which outperforms traditional pretraining methods on several downstream tasks. The MP-MAE builds upon the ConvNeXt V2 architecture and is trained on a diverse multi-modal pretraining dataset called MMEarth, comprising 1.2 million locations. By leveraging multiple pretext tasks, the approach demonstrates improved linear probing performance, label efficiency, and parameter efficiency compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a large-scale, diverse multi-modal pretraining dataset using Earth observation data, which can be used for various applications. It proposes a new type of masked autoencoder model that can learn general-purpose representations from optical satellite images. The approach outperforms other models on image classification and semantic segmentation tasks. |
Keywords
» Artificial intelligence » Autoencoder » Image classification » Mae » Multi modal » Pretraining » Semantic segmentation
