SatireDecoder: Visual Cascaded Decoupling for Enhancing Satirical Image Comprehension

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 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.