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English(EN) M2I2HA: Multi-modal Object Detection Based on Intra- and Inter-Modal Hypergraph Attention

M2I2HA网络利用超图理论推进多模态目标检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的多模态目标检测网络M2I2HA，该网络利用超图理论来改进特征提取和跨模态对齐。该方法通过捕获不同数据模态内部和之间复杂的、多对多的关系，解决了现有方法（如CNN、Transformer和状态空间模型）的局限性。在公开数据集上的实验表明，M2I2HA在多模态目标检测方面取得了最先进的性能。 AI

影响 这种基于超图的新方法可以提高复杂环境中目标检测系统的准确性和鲁棒性。

排序理由 该集群包含一篇详细介绍新模型及其性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

M2I2HA网络利用超图理论推进多模态目标检测

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该集群包含一篇详细介绍新模型及其性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xiaofan Yang, Yubin Liu, Wei Pan, Guoqing Chu, Junming Zhang, Jie Zhao, Zhuoqi Man, Xuanming Cao ·

    M2I2HA：基于模态内和模态间超图注意力的多模态目标检测

    arXiv:2601.14776v3 Announce Type: replace Abstract: Recent advances in multi-modal detection have significantly improved detection accuracy in challenging environments (e.g., low light, overexposure). By integrating RGB with modalities such as thermal and depth, multi-modal fusio…

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