MMSD3.0：面向真实世界多模态讽刺检测的多图像基准
MMSD3.0: A Multi-Image Benchmark for Real-World Multimodal Sarcasm Detection
该论文提出的多图像基准和跨图像推理模型（CIRM）为视觉-语言任务中多图像语义关系建模提供了可复用的评测平台和融合思路，尤其适用于需要跨图像推理的讽刺检测、情感分析等场景。
原文依据 we introduce MMSD3.0, a new benchmark composed entirely of multi-image samples curated from tweets and Amazon reviews.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管多模态讽刺检测已取得进展，但现有数据集和方法主要集中于单图像场景，忽略了多图像间潜在的语义和情感关系。这导致在真实世界环境中由多图像线索触发讽刺的建模存在空白。为弥补这一空白，我们引入了MMSD3.0，这是一个完全由从推文和亚马逊评论中整理的多图像样本组成的新基准。我们进一步提出了跨图像推理模型（CIRM），集成双阶段桥接模块和相关性引导融合模块，以建模图像间依赖和跨模态对应关系。作为补充，我们建立了一套全面且具有代表性的强基线，并进行了大量实验，表明MMSD3.0是一个有效且可靠的基准，能更好地反映真实世界条件。此外，CIRM在MMSD、MMSD2.0和MMSD3.0上均展现出最优性能，验证了其在单图像和多图像场景中的有效性。数据集和代码已公开于https://github.com/ZHCMOONWIND/MMSD3.0。
英文原文摘要 1131 字符
Despite progress in multimodal sarcasm detection, existing datasets and methods predominantly focus on single-image scenarios, overlooking potential semantic and affective relations across multiple images. This leaves a gap in modeling cases where sarcasm is triggered by multi-image cues in real-world settings. To bridge this gap, we introduce MMSD3.0, a new benchmark composed entirely of multi-image samples curated from tweets and Amazon reviews. We further propose a Cross-Image Reasoning Model (CIRM), integrating a Dual-Stage Bridge Module and Relevance-Guided Fusion Module to model inter-image dependencies and cross-modal correspondences. Complementarily, we establish a comprehensive suite of strong and representative baselines and conduct extensive experiments, showing that MMSD3.0 is an effective and reliable benchmark that better reflects real-world conditions. Moreover, CIRM demonstrates state-of-the-art performance across MMSD, MMSD2.0, and MMSD3.0, validating its effectiveness in both single-image and multi-image scenarios. Dataset and code are publicly available at https://github.com/ZHCMOONWIND/MMSD3.0.