SatireDecoder: 视觉级联解耦以增强讽刺图像理解
SatireDecoder: Visual Cascaded Decoupling for Enhancing Satirical Image Comprehension
AI总结：
SatireDecoder通过视觉级联解耦和不确定性分析策略，提升讽刺图像理解的准确性和语义层次。
摘要：
讽刺是一种结合幽默与隐含批评的艺术表达形式，具有重要的社会价值，通过揭示社会问题来引起关注。尽管其在文化和社会层面具有重要价值，但目前的视觉-语言模型在纯视觉形式的讽刺理解上仍面临巨大挑战。这项任务不仅需要检测讽刺，还需要解读其微妙含义并识别所涉及的实体。现有模型往往无法有效整合局部实体关系与全局上下文，导致误解、理解偏差和幻觉。为了解决这些限制，我们提出了SatireDecoder，一种无需训练的框架，旨在增强讽刺图像理解。我们的方法提出了一种多代理系统，通过视觉级联解耦将图像分解为细粒度的局部和全局语义表示。此外，我们引入了一种由不确定性分析引导的推理策略，将复杂的讽刺理解过程分解为具有最小不确定性的连续子任务。我们的方法显著提高了解释准确性，同时减少了幻觉。实验结果验证了SatireDecoder在理解视觉讽刺方面的优越性，为视觉-语言推理在微妙、高层次语义任务中提供了有前途的方向。
英文摘要：
Satire, a form of artistic expression combining humor with implicit critique, holds significant social value by illuminating societal issues. Despite its cultural and societal significance, satire comprehension, particularly in purely visual forms, remains a challenging task for current vision-language models. This task requires not only detecting satire but also deciphering its nuanced meaning and identifying the implicated entities. Existing models often fail to effectively integrate local entity relationships with global context, leading to misinterpretation, comprehension biases, and hallucinations. To address these limitations, we propose SatireDecoder, a training-free framework designed to enhance satirical image comprehension. Our approach proposes a multi-agent system performing visual cascaded decoupling to decompose images into fine-grained local and global semantic representations. In addition, we introduce a chain-of-thought reasoning strategy guided by uncertainty analysis, which breaks down the complex satire comprehension process into sequential subtasks with minimized uncertainty. Our method significantly improves interpretive accuracy while reducing hallucinations. Experimental results validate that SatireDecoder outperforms existing baselines in comprehending visual satire, offering a promising direction for vision-language reasoning in nuanced, high-level semantic tasks.
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。
