From Recognition to Reasoning: Advancing Multimodal Harmful Meme Detection via Chain-of-Thought Alignment
Researchers have created a new dataset called MemeMind to help detect harmful memes. The dataset includes over 20,000 publicly available memes and provides detailed annotations of the content's harm level, intentions, and underlying meanings. A framework called MemeGuard is also proposed to identify and reason about harmful meme content. Experimental results show that MemeGuard outperforms existing methods on the MemeMind dataset.
Researchers have created a new dataset called MemeMind to help detect harmful memes. The dataset includes over 20,000 publicly available memes and provides detailed annotations of the content's harm level, intentions, and underlying meanings. A framework called MemeGuard is also proposed to identify and reason about harmful meme content. Experimental results show that MemeGuard outperforms existing methods on the MemeMind dataset.
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Why it matters: This work matters because it addresses a critical challenge in AI: detecting implicit harm in multimodal content like memes. The authors' approach can help improve online safety by providing more accurate and interpretable models for identifying harmful meme content.
Source: https://arxiv.org/abs/2506.18919
This article was originally published at: https://arxiv.org/abs/2506.18919