MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection
For researchers in multimodal stance detection, this work provides a novel framework that addresses contextual grounding and cross-modal ambiguity, though it is incremental as it combines existing techniques.
The paper tackles multimodal stance detection by proposing a multi-agent framework with retrieval augmentation, achieving significant improvements over state-of-the-art baselines across five datasets.
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.