M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought (2024.emnlp-main)
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. |
| Approach: | They propose a framework for misogynous meme identification using a multimodal multimodal prompting principle and a CLIP-based classifier. |
| Outcome: | The proposed framework performs well on the SemEval-2022 task 5 dataset, and is generalizable across different datasets. |
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| Challenge: | Existing methods to detect hate speech on social media are limited by heuristic graph construction, shallow modality fusion, and instance-level reasoning. |
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| Challenge: | a novel dataset of text-embedded images associated with the LGBTQ+ Pride movement is presented in this paper . a new framework for analyzing text-based images is proposed to address this challenge . |
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Md Ayon Mia, Akm Moshiur Rahman Mazumder, Khadiza Sultana Sayma, Md Fahim, Md Tahmid Hasan Fuad, Muhammad Ibrahim Khan, Akmmahbubur Rahman
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| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have shifted visual reasoning from tool-calling to end-to-end perceptionreasoning. |
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Chain-of-Thought Embeddings for Stance Detection on Social Media (2023.findings-emnlp)
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| Challenge: | Stance detection on social media platforms like Twitter is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit stance labels. |
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A Multimodal Framework to Detect Target Aware Aggression in Memes (2024.eacl-long)
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| Challenge: | Recent research on memes’ detrimental facets is skewed towards high-resource languages, such as Bengali. |
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