| Challenge: | Existing methods for integrating hate information from different modalities ignore the modality uncertainty caused by the contribution degree of each modality to hate sentiment. |
| Approach: | They propose an Uncertainty-guided Modal Rebalance framework for hateful memes detection . they propose to combine cross-modal fusion features with unimodal features . |
| Outcome: | The proposed framework produces state-of-the-art performance on four widely-used datasets. |
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Uncertainty-Aware Cross-Modal Alignment for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Existing methods for detecting hate speech ignore misalignment and uncertainty between modalities . social media platforms have become conduits for the rapid dissemination of hate speech . |
| Approach: | They propose an uncertainty-aware cross-modal alignment framework for hate speech detection that minimizes the misalignment of image and text in memes. |
| Outcome: | The proposed framework produces a competitive performance compared with existing methods. |
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. |
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. |
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. |
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. |
| Outcome: | The proposed tool improves classification accuracy for unimodal and multimodal models . the proposed tool can be used to model the contrast between image content and overlayed text . |
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. |
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. |
Bridging Modality Gap for Effective Multimodal Sentiment Analysis in Fashion-related Social Media (2025.coling-main)
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| Challenge: | Existing sentiment analysis tasks focus on text comprehension, but visual content is important for emotional expression. |
| Approach: | They propose a multimodal framework that integrates information from various modalities for sentiment classification of fashion posts. |
| Outcome: | The proposed framework outperforms existing unimodal and multimodal baselines on a comprehensive dataset and significantly outperformed existing unilmodal and multiple modal frameworks. |
Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection focus on fusing text and image information to establish cross-modal correlations, overlooking the significance of original unimodal incongruity information. |
| Approach: | They propose a multi-modal incongruity learning module to capture inconcluity information simultaneously at the text-level, image-level and cross-modal-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a publicly available dataset. |
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. |