Summary of A Causally Informed Pretraining Approach For Multimodal Foundation Models: Applications in Remote Sensing, by Praveen Ravirathinam et al.
A Causally Informed Pretraining Approach for Multimodal Foundation Models: Applications in Remote Sensing
by Praveen Ravirathinam, Ankush Khandelwal, Rahul Ghosh, Vipin Kumar
First submitted to arxiv on: 29 Jul 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 proposes a novel self-supervised learning approach called Causally Informed Variable-Step Forecasting (CI-VSF) for pretraining foundation models. CI-VSF uses large-scale data and models forecasting as a conditional generation task, where driver variables inform the prediction of response variables. This approach demonstrates enhanced performance when finetuned on downstream tasks such as crop mapping, missing image prediction, soil moisture estimation, future image forecasting, and soil moisture forecasting compared to other pretraining approaches. The proposed strategy is applicable to any domain that involves known causal relationships amongst a set of variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better from big data. Right now, we use special tricks to make our computers smarter, but this doesn’t always work well with some types of data. To solve this problem, the researchers came up with a new way to train our computers using large amounts of data. This method is called CI-VSF and it’s like making predictions based on what we know about the world. It works really well for tasks like predicting crops, missing images, and soil moisture levels. The best part is that this approach can be used in many different fields where we have information about how things are related. |
Keywords
* Artificial intelligence * Pretraining * Self supervised
