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English(EN) Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognition

新模型通过不确定性量化增强情感识别能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种具有证据融合的多模态 Transformer (EMT-DS)，以提高情感识别的准确性，特别是在处理冲突的传感器数据时。该新模型通过区分证据不足和传感器噪声来量化不确定性，并使用 Dempster 组合规则来处理矛盾的输入。EMT-DS 在包括 IEMOCAP、DEAP、KEMDy20 和 CMU-MOSEI 在内的多个基准测试中表现出色，同时还显示出对噪声和模态缺失的鲁棒性。 AI

影响 提高了处理不确定或冲突数据的多模态 AI 系统的鲁棒性和准确性。

排序理由 详细介绍新模型及其在基准测试中性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新模型通过不确定性量化增强情感识别能力

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详细介绍新模型及其在基准测试中性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Muhammad Usama, Yeoreum Jung ·

    用于情感识别的具有 Dempster-Shafer 融合的证据多模态 Transformer

    arXiv:2610.13013v1 Announce Type: new Abstract: Softmax-based multimodal emotion recognition methods often assign high confidence even when sensor data are corrupted or contradictory, for example when facial expressions suggest positive valence while vocal prosody indicates negat…

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