All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes (2020.aacl-main)
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| Challenge: | Empirical results show the efficacy of our proposed multi-task framework over existing state-of-the-art systems. |
| Approach: | They propose a multi-task, multi-modal deep learning framework to solve multiple tasks simultaneously. |
| Outcome: | The proposed framework performs better than existing state-of-the-art systems on a complicated form of information, i.e., memes. |
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Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for harmful meme detection ignore in-depth cognition of meme text and image . authors propose a framework for learning reasonable thoughts from LLMs for better multimodal fusion . |
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