MER-Bench: A Comprehensive Benchmark for Multimodal Meme Reappraisal
Yiqi Nie, Fei Wang, Junjie Chen, Kun Li, Yudi Cai, Dan Guo, Chenglong Li, and Meng Wang

TL;DR
This paper introduces MER-Bench, a comprehensive benchmark for evaluating multimodal meme reappraisal, focusing on transforming negative memes into positive ones while maintaining their structure and context, supported by a new evaluation framework.
Contribution
It presents a new task of meme reappraisal, constructs a detailed benchmark dataset with annotations, and proposes a structured evaluation framework for multimodal meme transformation.
Findings
Significant gaps in current models' ability to preserve structure and semantics.
The benchmark enables systematic evaluation of emotion control in meme editing.
Extensive experiments highlight challenges and future directions in multimodal meme reappraisal.
Abstract
Memes represent a tightly coupled, multimodal form of social expression, in which visual context and overlaid text jointly convey nuanced affect and commentary. Inspired by cognitive reappraisal in psychology, we introduce Meme Reappraisal, a novel multimodal generation task that aims to transform negatively framed memes into constructive ones while preserving their underlying scenario, entities, and structural layout. Unlike prior works on meme understanding or generation, Meme Reappraisal requires emotion-controllable, structure-preserving multimodal transformation under multiple semantic and stylistic constraints. To support this task, we construct MER-Bench, a benchmark of real-world memes with fine-grained multimodal annotations, including source and target emotions, positively rewritten meme text, visual editing specifications, and taxonomy labels covering visual type, sentiment…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Humor Studies and Applications · Mental Health via Writing
