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. |
| Outcome: | HateSieve features a new framework that creates semantically correlated memes and generates contextual embeddings for accurate meme segmentation. |
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Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning (2024.acl-long)
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| Challenge: | Existing systems for detecting hateful memes lack sensitivity to subtle differences in memes that are vital for correct hatefulness classification. |
| Approach: | They propose to construct a hatefulness-aware embedding space through retrieval-guided contrastive training to identify hatefulness based on data unseen in training. |
| Outcome: | The proposed system outperforms existing models on the HatefulMemes dataset with an AUROC of 87.0 and improves contextual understanding across domains. |
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. |
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Caption Enriched Samples for Improving Hateful Memes Detection (2021.emnlp-main)
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| Challenge: | Existing methods for classifying memes are difficult to perform, with human accuracy only about 85% . recent state-of-the-art models perform considerably less accurately, achieving up to 64.73% accuracy. |
| Approach: | They propose to use an off-the-shelf caption generator to capture the first image and overlayed text. |
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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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Deciphering Hate: Identifying Hateful Memes and Their Targets (2024.acl-long)
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| Challenge: | a growing body of research has focused on the negative aspects of memes in high-resource languages like Bengali . a new dataset for Bengali hateful memes is designed to detect their targeted entities . |
| Approach: | They propose a multimodal dataset that analyzes the modality of memes and compares them with other datasets. |
| Outcome: | The proposed dataset outperforms state-of-the-art datasets on Bengali hateful memes . the proposed dataset is generalizable on other low-resource hateful memes datasets compared with baselines based on the proposed model . |
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models? (2024.findings-eacl)
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| Challenge: | Existing studies show that only the textual component of hateful memes enables the multimodal classifier to generalize across domains while the image component proves highly sensitive to a specific training dataset. |
| Approach: | They propose to use only the textual component of hateful memes to generalize across different domains while the image component is highly sensitive to a specific training dataset. |
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Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection (2024.eacl-srw)
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| Challenge: | Existing approaches to multimodal hateful content detection focus on detecting hate speech from text-based content, but they fail to address modality-specific features. |
| Approach: | They propose a context-aware attention framework for multimodal hateful content detection that integrates an attention layer to meaningfully align the visual and textual features. |
| Outcome: | The proposed framework achieves F1-scores of 69.7% and 70.3% on two hateful meme datasets and shows 2.5% and 3.2% performance improvement over the state-of-the-art systems. |
MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification (2024.emnlp-main)
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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 . |
| Approach: | They propose a new dataset for machine learning that includes hate, targets of hate, stance, humor and a framework for efficient downstream learning while preserving the knowledge of the pre-trained CLIP model. |
| Outcome: | The proposed framework achieves superior performance on two real-world datasets. |
Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate (2021.findings-acl)
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| Challenge: | Imlicit hate content has unusual syntax, polysemic words, and fewer markers of prejudice, e.g., slurs . multimodal content is harder to detect than unimodal content, such as memes . |
| Approach: | They evaluate the role of semantic and multimodal context for detecting implicit and explicit hate . they find that all models perform better on content with full annotator agreement . |
| Outcome: | The proposed model outperforms other models on implicit and explicit hate detection tasks because of its lower propensity towards false positives. |
MUTE: A Multimodal Dataset for Detecting Hateful Memes (2022.aacl-srw)
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| Challenge: | social media has enabled information propagation at unprecedented rate, but also generated malign content, such as hateful memes . a multimodal hate speech dataset is used to study the impact of hateful content on society . current studies focus on monolingual memes, but existing models cannot provide accurate inferences based on code-mixed captions a study on Bengali memes shows that joint evaluation of visual and textual features significantly improves the hateful data classification . |
| Approach: | They propose to use a multimodal hate speech dataset to detect hateful memes . they use monolingual captions in English and Bengali to analyze the content . |
| Outcome: | The proposed dataset shows that evaluation of visual and textual features significantly improves the hateful memes classification compared to unimodal evaluation. |