CoDe-MAE achieves effective joint pretraining of heterogeneous high-resolution optical and SAR images via optical-anchored knowledge distillation, conditioned contrastive learning, and cross-modal degraded reconstruction, yielding better data efficiency and downstream performance than larger-scale基础
Sat- laspretrain: A large-scale dataset for remote sensing image understanding,
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
-
Better with Less: Tackling Heterogeneous Multi-Modal Image Joint Pretraining via Conditioned and Degraded Masked Autoencoder
CoDe-MAE achieves effective joint pretraining of heterogeneous high-resolution optical and SAR images via optical-anchored knowledge distillation, conditioned contrastive learning, and cross-modal degraded reconstruction, yielding better data efficiency and downstream performance than larger-scale基础