HQ-JEPA combines JEPA-style predictive self-supervision with cross-modal alignment and a SWAP-test-based quantum fidelity loss for learning representations from paired remote sensing imagery, reporting competitive results on GeoBench tasks.
Chal- lenges and proposed solutions in modeling multimodal data: A systematic review
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HQ-JEPA: Hybrid Quantum Joint-Embedding Predictive Architecture for Cross-Modal Remote Sensing Representation Learning
HQ-JEPA combines JEPA-style predictive self-supervision with cross-modal alignment and a SWAP-test-based quantum fidelity loss for learning representations from paired remote sensing imagery, reporting competitive results on GeoBench tasks.