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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.
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Cited by 2 Pith papers
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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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MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
MIND uses unlabeled similar memes, bidirectional AI insight derivation, and multi-agent debate to improve zero-shot harmful meme detection on HarM, FHM, and MAMI.
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