Research map / Vision-language and multimodal / Human and embodied modeling
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| multimodal pretraining (COCO) instead of unimodal pretraining | Visual BERT · Hateful Memes test | AUROC: 74.42 -> 75.44 | No clear effect |
| multimodal pretraining (Conceptual Captions) instead of unimodal pretraining | ViLBERT · Hateful Memes test | AUROC: 73.32 -> 74.52 | Helped |
| early fusion (MMBT-Region) instead of late fusion | BERT + image features · Hateful Memes test | AUROC: 69.30 -> 73.82 | Helped |
| region features instead of grid features | MMBT · Hateful Memes test | AUROC: 69.49 -> 73.82 | Helped |
| add image modality via late fusion to text BERT | Text BERT · Hateful Memes test | AUROC: 69.00 -> 69.30 | No clear effect |
Closest papers by meaning
- TRACE: Textual Relevance Augmentation and Contextual Encoding for Multimodal Hate Detection (AAAI 2026 · similarity 0.90)
- Hateful Meme Detection through Context-Sensitive Prompting and Fine-Grained Labeling (Student Abstract) (AAAI 2025 · similarity 0.89)
- Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media (AAAI 2024 · similarity 0.88)
- X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and a Novel LLM-Consulted Explanation Framework (AAAI 2026 · similarity 0.88)
- Spanning the Spectrum of Hatred Detection: A Persian Multi-Label Hate Speech Dataset with Annotator Rationales (AAAI 2024 · similarity 0.87)
- A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings (NeurIPS 2025 · similarity 0.83)
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