MM-StanceDet：基于检索的多模态多智能体立场检测
MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection
AI总结：
本文提出MM-StanceDet框架，通过整合检索增强、多模态分析代理、辩论阶段和自我反思，解决多模态立场检测中的上下文 grounding、跨模态解释模糊和单次推理脆弱问题，实验表明其在五个数据集上优于现有方法。
摘要：
多模态立场检测（MSD）对于理解公共讨论至关重要，但有效融合文本和图像，尤其是处理冲突信号仍具挑战性。现有方法常面临上下文 grounding、跨模态解释歧义和单次推理脆弱性等问题。为解决这些问题，我们提出检索增强的多模态多智能体立场检测（MM-StanceDet），一种新颖的多智能体框架，整合检索增强以实现上下文 grounding，专门化的多模态分析代理以实现精细解释，增强推理的辩论阶段以探索视角，以及自我反思以实现稳健裁决。在五个数据集上的广泛实验表明，MM-StanceDet显著优于现有最先进基线，验证了其多智能体架构和结构化推理阶段在解决复杂多模态立场挑战中的有效性。
英文摘要：
Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting signals, remains challenging. Existing methods often face difficulties with contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. To address these, we propose Retrieval-Augmented Multi-modal Multi-agent Stance Detection (MM-StanceDet), a novel multi-agent framework integrating Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, a Reasoning-Enhanced Debate stage for exploring perspectives, and Self-Reflection for robust adjudication. Extensive experiments on five datasets demonstrate MM-StanceDet significantly outperforms state-of-the-art baselines, validating the efficacy of its multi-agent architecture and structured reasoning stages in addressing complex multimodal stance challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
