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New framework MemeGuard advances harmful meme detection with reasoning annotations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MemeGuard, a novel multimodal framework designed to improve the detection of harmful memes. This framework is built upon MemeMind, a newly constructed large-scale dataset featuring detailed Chain-of-Thought reasoning annotations. MemeGuard utilizes a three-stage training process to enhance its capabilities in visual understanding, multimodal reasoning, and harmful content discrimination, outperforming existing state-of-the-art methods. AI

IMPACT Enhances multimodal content safety by improving the detection of subtle harmful content in memes.

RANK_REASON The cluster describes a new research paper introducing a novel dataset and framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework MemeGuard advances harmful meme detection with reasoning annotations

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The cluster describes a new research paper introducing a novel dataset and framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hexiang Gu, Qifan Yu, Yuan Liu, Zikang Li, Saihui Hou, Jian Zhao, Zhaofeng He ·

    From Recognition to Reasoning: Advancing Multimodal Harmful Meme Detection via Chain-of-Thought Alignment

    arXiv:2506.18919v5 Announce Type: replace-cross Abstract: As a multimodal communication medium that integrates images and text, memes often convey implicit harmful content through metaphors, satire, and humor, making harmful meme detection a complex and challenging task. Although…

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