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.
arXiv preprint arXiv:2112.04323 , year=
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VLMs recover reliable population-level trends in climate change visual discourse on social media even when per-image accuracy is only moderate.
citing papers explorer
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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.
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From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media
VLMs recover reliable population-level trends in climate change visual discourse on social media even when per-image accuracy is only moderate.