SatireDecoder: Visual Cascaded Decoupling for Enhancing Satirical Image Comprehension

TL;DR

This work tackles the challenging task of purely visual satire comprehension, where prior models struggle to reconcile local entity details with global context and suffer from hallucinations. It introduces SatireDecoder, a training-free framework that uses a multi-agent visual cascaded decoupling scheme to obtain fine-grained local/global semantic representations, followed by a chain-of-thought reasoning process guided by uncertainty analysis to reduce erroneous inferences. The method demonstrates improved interpretive accuracy and reduced hallucinations across baselines on the YesBut dataset, with thorough ablations and human evaluation supporting its effectiveness. By decoupling perception from reasoning and leveraging uncertainty-guided inference, SatireDecoder offers a scalable, cost-efficient direction for nuanced multimodal satire understanding in vision-language systems.

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.

Figures (4)

• Figure 1: SatireDecoder consists of a multi-agent visual cascaded decoupling module and an uncertainty analysis strategy, which together help MLLMs capture discrepancies between local entities and the global context, thereby enhancing models' ability to comprehend satirical images.
• Figure 2: In the multi-agent system, the Local Entities Extraction Agent simulates the InferoTemporal cortex (IT cortex) grill2004human, while the Global Semantics Extraction Agent simulates the Broca's area and the Wernicke's area jancke2021language. Additionally, the Discrepancy Analysis Agent imitates the function of the PreFrontal Cortex (PFC) and the Posterior Parietal Cortex (PPC) grill2004human.
• Figure 3: Framework of SatireDecoder. (1) Visual Cascaded Decoupling: A multi-agent system decouples the satirical image into fine-grained semantic representations. (2) Prompt Construction and CoT Reasoning: The fine-grained semantic representations form a structured prompt for CoT reasoning, breaking complex satire comprehension into three subtasks. (3) Inference Optimization with Uncertainty Analysis: progressively reduces uncertainty and hallucinations during reasoning.
• Figure 4: Visualization of the ablation study. "UA" represents Uncertainty Analysis. The red text indicates the hallucinations and misinterpretations of satirical meaning. The green text highlights the objects and the correct satirical meanings newly captured after the application of SatireDecoder and uncertainty analysis.