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English(EN) QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation

量子分类器以更少的参数提升场景图生成能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种用于场景图生成（SGG）的混合量子谓词分类器，以解决长尾谓词不平衡的挑战。这种新方法用量子谓词头（QP-Head）取代了CFEN模型中的经典谓词头。QP-Head显著减少了可训练参数的数量，同时提高了在Visual Genome 150数据集上的性能。 AI

影响 这项研究展示了一种利用量子计算原理来改进复杂视觉推理任务的参数高效方法。

排序理由 这是一篇研究论文，详细介绍了一种针对特定AI任务的新型混合量子方法。

在 arXiv cs.LG 阅读 →

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量子分类器以更少的参数提升场景图生成能力

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Prerana Ramkumar, Nouhaila Innan, Muhammad Shafique ·

    QPredSGG：用于长尾场景图生成的混合量子谓词学习

    arXiv:2606.04689v1 Announce Type: cross Abstract: Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail predicate imbalance. Classical SGG models frequently rely on dataset statistics, …

  2. arXiv cs.LG TIER_1 English(EN) · Muhammad Shafique ·

    QPredSGG：用于长尾场景图生成的混合量子谓词学习

    Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail predicate imbalance. Classical SGG models frequently rely on dataset statistics, leading to biased predictions toward frequent rela…

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