Summary of Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment, by Tengjun Huang
Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment
by Tengjun Huang
First submitted to arxiv on: 28 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 The authors propose a novel method called Harmonized Transfer Learning and Modality Alignment (HarMA) to tackle the challenges of multimodal transfer learning in remote sensing tasks. HarMA aims to simultaneously satisfy task constraints, modality alignment, and single-modality uniform alignment while minimizing training overhead through parameter-efficient fine-tuning. The method achieves state-of-the-art performance in two popular multimodal retrieval tasks without requiring external data for training. HarMA can be integrated into existing multimodal pretraining models, making it a versatile solution for downstream tasks. The proposed approach leverages the benefits of visual and language pretraining while addressing the limitations of same-modality embeddings clustering together. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Harmonized Transfer Learning and Modality Alignment (HarMA) is a new way to help big models work better on different tasks, like recognizing objects in satellite images or identifying sounds. This method makes sure that the model learns from all types of data at the same time, which helps it become more accurate without needing extra training. HarMA works really well and can be used with many existing models, making it a useful tool for scientists. |
Keywords
» Artificial intelligence » Alignment » Clustering » Fine tuning » Parameter efficient » Pretraining » Transfer learning
