OceanMAE is an ocean-adapted masked autoencoder that adds physically meaningful auxiliary descriptors during self-supervised pre-training on Sentinel-2 data and shows improved marine segmentation performance on downstream benchmarks.
Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
OceanMAE: A Foundation Model for Ocean Remote Sensing
OceanMAE is an ocean-adapted masked autoencoder that adds physically meaningful auxiliary descriptors during self-supervised pre-training on Sentinel-2 data and shows improved marine segmentation performance on downstream benchmarks.