K. Schulz

Attention-driven Cross-modal Self-supervised Learning for Label-efficient Hyperspectral-LiDAR DSM Classification

This work proposes a self-supervised processing framework based on cross-modal attention that effectively fuses features at multiple levels, thereby exploiting complementary information across data streams and lays the groundwork for fusion methods that effectively exploit inter-stream data relationships to encourage c...