Meme Detection

Meme detection, specifically focusing on identifying hateful or toxic memes, is a rapidly evolving field aiming to develop robust and explainable systems for automatically classifying multimodal content. Current research heavily utilizes large multimodal models (LMMs) and vision-language models (VLMs), often employing techniques like contrastive learning, prompt engineering, and modular network architectures to improve accuracy and efficiency, particularly in low-resource settings. This research is crucial for mitigating the spread of harmful online content and improving content moderation strategies across social media platforms, while also advancing our understanding of how toxicity manifests in multimodal communication.