SatireDecoder: Visual Cascaded Decoupling for Enhancing Satirical Image Comprehension
Yue Jiang, Haiwei Xue, Minghao Han, Mingcheng Li, Xiaolu Hou, Dingkang Yang, Lihua Zhang, Xu Zheng

TL;DR
SatireDecoder is a novel framework that improves understanding of satirical images by decomposing visual information into local and global features and applying chain-of-thought reasoning, leading to better interpretive accuracy.
Contribution
It introduces a training-free, multi-agent visual cascaded decoupling system combined with uncertainty-guided reasoning for enhanced satire comprehension.
Findings
Outperforms existing models in satire understanding accuracy
Reduces hallucinations and misinterpretations in visual satire analysis
Demonstrates effectiveness of cascaded decoupling and reasoning strategies
Abstract
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Language, Metaphor, and Cognition · Humor Studies and Applications
