| Challenge: | Existing models that detect misogyny are not able to detect unintended biases in memes, perpetuating harmful stereotypes and reinforcing negative attitudes. |
| Approach: | They propose to measure and mitigate unintentional bias in misogynous memes detection models by using a contextualized scene graph-based multimodal network (CTXSGMNet) they also evaluate their generalizability by evaluating their performance on a few benchmark meme datasets. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SemEval-2022 Task 5 (MAMI task) dataset, showcasing its promising performance in terms of Equity of Odds and F1 score. |
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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. |
| Approach: | They propose a multimodal framework for detecting sexism and misogyny using a graph reasoning mechanism that can be used to train multiple visual-textual fusion strategies. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on MAMI and EXIST benchmarks while achieving faster training convergence. |
From Laughter to Inequality: Annotated Dataset for Misogyny Detection in Tamil and Malayalam Memes (2024.lrec-main)
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Rahul Ponnusamy, Kathiravan Pannerselvam, Saranya R, Prasanna Kumar Kumaresan, Sajeetha Thavareesan, Bhuvaneswari S, Anshid K.a, Susminu S Kumar, Paul Buitelaar, Bharathi Raja Chakravarthi
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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. |
Seeing Through VisualBERT: A Causal Adventure on Memetic Landscapes (2024.findings-emnlp)
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| Challenge: | Existing models for detecting offensive memes lack transparency and are often unreliability in safety-critical applications. |
| Approach: | They propose a framework that uses a Structural Causal Model to predict the class of an input meme based on meme input and causal concepts, allowing for transparent interpretation. |
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MemeDetoxNet: Balancing Toxicity Reduction and Context Preservation (2025.findings-acl)
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| Challenge: | Toxic memes spread harmful and offensive content and pose a significant challenge in online environments. |
| Approach: | They propose a framework to mitigate toxicity in toxic memes by leveraging a set of pre-trained models that can interpret the visual and textual components of memes. |
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DISARM: Detecting the Victims Targeted by Harmful Memes (2022.findings-naacl)
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| Challenge: | DISARM is a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and then incorporates a novel contextualized deep neural network to classify whether the meme intends to harm these entities. |
| Approach: | They propose a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and incorporates a novel contextualized deep neural network to classify whether the meme intends to harm them. |
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MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization (2023.acl-long)
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| Challenge: | Besides digital archiving of memes and their metadata, there is no efficient way to deduce a meme’s context dynamically. |
| Approach: | They propose a task to mine the context that succinctly explains the background of a meme and a related document to capture cross-modal semantic dependencies between the meme and the context. |
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MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets (2021.findings-emnlp)
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Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing number of harmful memes are being used for trolling, cyberbullying and abuse . a new approach to detect harmful meme images and texts is emerging . |
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MemeIntel: Explainable Detection of Propagandistic and Hateful Memes (2025.emnlp-main)
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| Challenge: | Existing methods for label detection and explanation generation have been limited in understanding complex issues . identifying propaganda and hate in memes is essential for combating misinformation and minimizing harm . |
| Approach: | They propose an explanation-enhanced dataset for propaganda memes in Arabic and hateful memes on English to solve these tasks. |
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A Context-Aware Contrastive Learning Framework for Hateful Meme Detection and Segmentation (2025.findings-naacl)
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| Challenge: | Empirical experiments show HateSieve surpasses existing LMMs in performance with fewer trainable parameters . |
| Approach: | They propose a framework to enhance detection and segmentation of hateful elements in memes by creating a triplet dataset and an Image-Text Alignment module. |
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