Papers with meme detection
MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection (2025.acl-long)
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| Challenge: | a rapid expansion of memes on social media highlights the need for effective methods to detect harmful content. |
| Approach: | They propose a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. |
| Outcome: | The proposed framework outperforms existing zero-shot approaches on three meme datasets. |
Towards Low-Resource Harmful Meme Detection with LMM Agents (2024.emnlp-main)
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| Challenge: | Existing methods for harmful meme detection are limited due to the dynamic nature of memes . eliciting knowledge-revising behavior within the LMM agent is a key factor in achieving this goal . |
| Approach: | They propose an agency-driven framework for low-resource harmful meme detection . they use annotated memes to leverage label information as auxiliary signals for model . |
| Outcome: | The proposed framework achieves superior performance than state-of-the-art methods on the low-resource harmful meme detection task. |
Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: A Case Study on Hateful Memes (2024.lrec-main)
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| Challenge: | Causal analyses define semantics, while gradient-based methods are essential to eXplainable AI (XAI), interpreting the model’s ‘black box’. |
| Approach: | They propose to integrate causal analysis and XAI to integrate a model's mechanisms into their analysis by integrating a dataset of hateful meme detection models. |
| Outcome: | The proposed model can detect hateful memes using intersectionality principles and summarized attention scores highlight distinct behaviors of three Transformer models. |
CM-Off-Meme: Code-Mixed Hindi-English Offensive Meme Detection with Multi-Task Learning by Leveraging Contextual Knowledge (2024.lrec-main)
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| Challenge: | Existing studies on detecting offensive memes have focused on identifying them as implicit and explicit . detecting memes requires contextual knowledge, but there is no such dataset for the code-mixed Hindi-English domain. |
| Approach: | They propose an end-to-end multitask model that integrates contextual knowledge and psycho-linguistic knowledge to detect offensive memes. |
| Outcome: | The proposed model is able to detect offensive memes and explicit memes in a large-scale dataset. |
Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding (2026.acl-long)
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| Challenge: | Existing methods for identifying harmful memes rely on modal alignment or black-box classifiers . BPDMoE-Hate provides visual explanations for viewpoint selection and hierarchical structuring . |
| Approach: | They propose a framework that conceptualizes harmful meme detection as a process of "viewpoint decoupling and hierarchical fusion" they propose BPDMoE-Hate, which generates adversarial binary perspectives via VLMs and incorporates an adaptive viewpoint gating to facilitate viewpoint selection. |
| Outcome: | The proposed framework surpasses existing methods in performance and provides visual explanations for viewpoint selection and hierarchical structuring. |
Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection (2025.emnlp-main)
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| Challenge: | Large Multimodal Models (LMMs) have shown promise in hateful meme detection, but they face limitations like sub-optimal performance and limited out-of-domain generalization capabilities. |
| Approach: | They propose a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. |
| Outcome: | The proposed framework outperforms larger agentic systems in detecting hateful memes under adversarial attacks while maintaining the general vision-language capabilities of LMMs. |
DR-HM: Distill-then-Reinforce Training with Cognition-Aware Data Synthesis for Harmful Meme Detection (2026.findings-acl)
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Zihan Cheng, Jianxiang Ma, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Daling Wang, Yifei Zhang, Mingfu Zhang
| Challenge: | Current methods for harmful meme detection lack the knowledge required to identify such hate . current methods lack the ability to identify cultural stereotypes and visual metaphors . |
| Approach: | They propose a framework that decomposes meme analysis into a human-inspired reasoning process . they propose DR-HM to transfer knowledge from closed-source models while mitigating biases . |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |