MM-StanceDet: New Multi-Agent Framework for Multimodal Stance Detection
Researchers have introduced MM-StanceDet, a novel multi-agent framework for multimodal stance detection (MSD) that integrates retrieval augmentation, specialized analysis agents, reasoning debate, and self-reflection. The system addresses challenges in fusing text and image signals, particularly when they conflict. Experiments on five datasets show it outperforms state-of-the-art baselines. The paper is published on arXiv under computer science and artificial intelligence categories.
Key facts
- MM-StanceDet is a retrieval-augmented multi-modal multi-agent stance detection framework.
- It integrates retrieval augmentation for contextual grounding.
- It uses specialized multimodal analysis agents for nuanced interpretation.
- It includes a reasoning-enhanced debate stage for exploring perspectives.
- It incorporates self-reflection for robust adjudication.
- The system addresses challenges in fusing text and image with conflicting signals.
- Extensive experiments on five datasets show significant outperformance over state-of-the-art baselines.
- The paper is available on arXiv under Computer Science > Artificial Intelligence.
Entities
Institutions
- arXiv