MemeMind: A Large-Scale Multimodal Dataset with Chain-of-Thought Reasoning for Harmful Meme Detection

1. 1Collect and annotate a large-scale dataset of memes with images and text
2. 2Apply chain-of-thought reasoning to capture implicit harmful content and nuanced semantics
3. 3Use the dataset to fine-tune multimodal models for harmful meme detection
4. 4Evaluate the performance of the models on the dataset to identify areas for improvement

AI engineers and researchers working on multimodal models and harmful content detection can benefit from this dataset to improve their models' accuracy and interpretability

💡 Chain-of-thought reasoning can be effective in capturing implicit harmful content and nuanced semantics in memes

🚨 Detecting harmful memes just got easier with MemeMind, a new large-scale multimodal dataset! 💡