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English(EN) HCIG: A Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detection

新型图网络增强了多模态讽刺和网络欺凌的检测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了两种新颖的框架HCIG和GCCN，旨在改进多模态讽刺和网络欺凌的检测。HCIG（分层跨模态不一致图网络）使用图注意力网络在词元、短语和全局级别上对不一致性进行建模。GCCN（基于图的跨模态矛盾网络）采用基于图的推理和矛盾感知池化。这两种模型都旨在比传统融合方法更有效地捕捉文本和视觉信息之间的语义不一致性。在基准数据集上的实验表明，HCIG在讽刺检测方面取得了高准确率，而GCCN在网络欺凌检测方面表现出色。 AI

影响 这些基于图的方法为理解多模态内容提供了更细致的方法，有可能提高AI理解在线复杂社交交互的能力。

排序理由 该集群描述了一篇提出针对特定AI任务的新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新型图网络增强了多模态讽刺和网络欺凌的检测能力

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该集群描述了一篇提出针对特定AI任务的新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bhavana Verma, Priyanka Meel, Dinesh Kumar Vishwakarma ·

    HCIG：一种用于多模态讽刺和网络欺凌检测的分层跨模态不一致图网络

    arXiv:2607.16076v1 Announce Type: cross Abstract: Multimodal sarcasm and cyberbullying detection remain challenging because the intended meaning often emerges from incongruity between textual and visual information rather than from either modality alone. Existing multimodal appro…

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