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English(EN) HQ-JEPA: Hybrid Quantum Joint-Embedding Predictive Architecture for Cross-Modal Remote Sensing Representation Learning

混合量子经典模型推动遥感AI发展

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了HQ-JEPA，一种用于从跨模态遥感数据中学习表示的新型混合量子经典架构。该框架通过整合量子相似性度量和多个自监督目标，增强了联合嵌入预测架构。在GeoBench任务上进行评估，HQ-JEPA在与现有基础模型相比时表现出竞争力，突显了将量子计算原理融入遥感AI的潜力。 AI

影响 引入了新颖的受量子启发的（quantum-inspired）技术，以改进AI驱动的遥感分析。

排序理由 该集群包含一篇详细介绍新AI架构的研究论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

混合量子经典模型推动遥感AI发展

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报道来源 [3]

  1. arXiv cs.CV TIER_1 English(EN) · Md Aminur Hossain, Ayush V. Patel, Nitant Dube, Biplab Banerjee ·

    CR-JEPA：用于遥感图像检索的跨模态联合嵌入预测学习

    arXiv:2606.00706v1 Announce Type: new Abstract: Cross-modal remote sensing image retrieval aims to retrieve semantically related scenes across heterogeneous sensing modalities. This remains challenging because paired observations may differ substantially in imaging physics, spati…

  2. arXiv cs.CV TIER_1 English(EN) · Md Aminur Hossain, Ayush V. Patel, Sanjay K. Singh, Biplab Banerjee ·

    HQ-JEPA：混合量子联合嵌入预测架构用于跨模态遥感表示学习

    arXiv:2605.31068v1 Announce Type: new Abstract: We introduce HQ-JEPA, a hybrid quantum-classical joint-embedding predictive architecture for cross-modal remote sensing representation learning. The proposed framework extends JEPA-style masked latent prediction to paired Sentinel-1…

  3. arXiv cs.CV TIER_1 English(EN) · Biplab Banerjee ·

    HQ-JEPA：混合量子联合嵌入预测架构用于跨模态遥感表示学习

    We introduce HQ-JEPA, a hybrid quantum-classical joint-embedding predictive architecture for cross-modal remote sensing representation learning. The proposed framework extends JEPA-style masked latent prediction to paired Sentinel-1 and Sentinel-2 imagery by predicting masked tar…

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