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Enhance Multimodal Transformer With External Label And In-Domain Pretrain: Hateful Meme Challenge Winning Solution
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Enhance Multimodal Transformer With External Label And In-Domain Pretrain: Hateful Meme Challenge Winning Solution
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Hateful meme detection is a new research area recently brought out that requires both visual, linguistic understanding of the meme and some background knowledge to performing well on the task. This technical report summarises the first place solution of the Hateful Meme Detection Challenge 2020, which extending state-of-the-art visual-linguistic transformers to tackle this problem. At the end of the report, we also point out the shortcomings and possible directions for improving the current methodology.
Forward citations
Cited by 3 Pith papers
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Flamingo: a Visual Language Model for Few-Shot Learning
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Unpacking Hateful Memes: Presupposed Context and False Claims
A hateful-meme detector that combines presupposed-context fusion, LLM-based social perception, and cross-modal reference graphs outperforms prior models on three benchmarks and transfers to fake news.
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Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities
A PRISMA-based survey of 158 computational works on toxic meme detection introduces a new toxicity taxonomy and a framework linking target, intent, and conveyance tactics while noting trends in LLMs and cross-modal methods.
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