A season-aware multimodal foundation model for remote sensing, built by fusing optical and SAR tokens across seasons during masked autoencoder pretraining, achieves state-of-the-art or near-best scores on several downstream benchmarks.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing
A season-aware multimodal foundation model for remote sensing, built by fusing optical and SAR tokens across seasons during masked autoencoder pretraining, achieves state-of-the-art or near-best scores on several downstream benchmarks.