Abstract
How to build machine learning models from few annotations is an open research question. This article shows an application of a meta-learning algorithm (REPTILE) to solve the problem of object segmentation. We evaluate how using REPTILE during a pre-training phase accelerates the learning process without loosing performance of the resulting segmentation in poor labeling conditions, and compare these results against training the detectors using basic transfer learning. Two scenarios are tested: (i) how segmentation performance evolves through training epochs with a fixed amount of labels and (ii) how segmentation performance improves with an increasing amount of labels after a fixed amount of epochs. The results suggest that REPTILE is useful making learning faster in both cases.
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Notes
- 1.
In aerial images it translates into different image ground resolutions and object scales.
- 2.
- 3.
Not anymore, given the increasing availability of electronic devices capable of capturing images (e.g., smartphones, satellites) nowadays.
- 4.
There are sub-datasets with diverse images of the same type at different resolutions.
- 5.
Annotation may be very time consuming, and that is the reason why we only tested one dataset.
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Acknowledgments
This work was supported by the Swiss Space Center (SERI/SSO MdP program). All the experiments shown in this paper were performed thanks to a thight collaboration with the company Picterra (https://picterra.ch/). Picterra provided all the datasets used, and participated in constructive discussion about how to deal with large images and how to build object detectors from few examples.
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Satizábal, H.F., Perez-Uribe, A. (2021). Learning Image Segmentation from Few Annotations. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_42
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