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
2020 ieee grss data fusion contest,
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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