English(EN) Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoning

新框架通过DPO和可解释推理改进多模态灾害评估

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种新颖的两阶段多模态灾害严重性评估训练框架，该框架整合了监督微调（SFT）和直接偏好优化（DPO）。该方法利用统一的数据构建流程生成两个数据集：用于SFT的ReasoningSet和用于DPO对齐的PreferenceSet。实验表明，在分类准确性和解释质量方面均有显著改进，后续的DPO对齐进一步增强了可解释性。通过在InternVL-3-8B和LLaVA-1.5-7B上的跨模型验证，证明了该框架的鲁棒性，从而能更好地检测代表性不足的损坏案例，并使模型推理与人类判断更加一致。 AI

影响增强了人工智能系统在灾害管理等关键应用中的可靠性和可解释性。

排序理由该集群包含一篇详细介绍新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yuanjun Zhang, Fuzel Ahamed Shaik, Suvojit Acharjee, Fahad Khalid, Mourad Oussalah · 2026-09-02 04:00

通过偏好优化和可解释的视觉-语言推理实现可靠的多模态灾害严重程度评估

arXiv:2609.00879v1 Announce Type: new Abstract: Reliable disaster damage assessment requires models that provide both accurate predictions and transparent explanations. However, existing multimodal approaches are limited by scarce annotated data and insufficient evaluation of rea…

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通过偏好优化和可解释的视觉-语言推理实现可靠的多模态灾害严重程度评估

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