Computational Meme Understanding: A Survey
Khoi P. N. Nguyen, Vincent Ng
2024年份
3被引次数
5顶会引用
摘要
Computational Meme Understanding, which concerns the automated comprehension of memes, has garnered interest over the last four years and is facing both substantial opportunities and challenges. We survey this emerging area of research by first introducing a comprehensive taxonomy for memes along three dimensions -forms, functions, and topics. Next, we present three key tasks in Computational Meme Understanding, namely, classification, interpretation, and explanation, and conduct a comprehensive review of existing datasets and models, discussing their limitations. Finally, we highlight the key challenges and recommend avenues for future work.
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引用它的顶会 Paper5
- ExPO-HM: Learning to Explain-then-Detect for Hateful Meme DetectionJingbiao Mei, Mingsheng Sun, Jinghong Chen, Pengda Qin 等ICLR 2026 · 被引用 8 次
- MGHFT: Multi-Granularity Hierarchical Fusion Transformer for Cross-Modal Sticker Emotion RecognitionJian Chen, Yuxuan Hu, Haifeng Lu, Wei Wang 等ACM MM 2025 · 被引用 5 次
- MemeQA: Holistic Evaluation for Meme UnderstandingKhoi P. N. Nguyen, Terrence Li, Derek Lou Zhou, Gabriel Xiong 等ACL 2025 · 被引用 3 次
- Read as You See: Guiding Unimodal LLMs for Low-Resource Explainable Harmful Meme DetectionFengjun Pan, Xiaobao Wu, Tho Quan, Anh Tuan LuuWWW 2026 · 被引用 2 次
- Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme DetectionJingbiao Mei, Jinghong Chen, Guangyu Yang, Weizhe Lin 等EMNLP 2025 · 被引用 2 次
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