I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition
Multimodal memes are dynamic and often require up-to-date background knowledge for interpretation. Existing methods overlook such knowledge or rely on fixed parametric knowledge from pre-trained models, which may be incomplete or outdated for emerging memes. We propose Query Retrieve Conclude (QRC), a zero-shot framework that identifies missing knowledge, retrieves open-web evidence, and synthesizes evidence-grounded knowledge for meme understanding and detection. We also introduce a curated benchmark of memes from 2024 to 2026 with external background annotations. Experiments on three understanding datasets and five detection tasks show improvements over zero-shot baselines.
[2606.05316] I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition
[Submitted on 3 Jun 2026]
Title:I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition
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Abstract:Multimodal memes are dynamic and often require up to date background knowledge for interpretation. Existing methods often overlook such knowledge or rely on fixed parametric knowledge of pretrained models that may be incomplete, outdated, or unavailable for emerging memes. We introduce Query Retrieve Conclude, a zero shot framework that identifies missing knowledge, retrieves open web evidence, and synthesizes evidence grounded background knowledge for meme understanding and detection. We also introduce a curated meme understanding benchmark of recent memes from 2024 to 2026 with external background knowledge annotations. Experiments on three meme understanding datasets and five meme detection tasks show that our framework improves knowledge recovery, meme understanding and downstream detection over zero shot baselines.
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Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.05316 [cs.AI]
(or arXiv:2606.05316v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.05316
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shanhong Liu [view email] [v1] Wed, 3 Jun 2026 18:06:14 UTC (24,864 KB)
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