Multimedia Automatic Misogyny Identification

Multimedia automatic misogyny identification focuses on developing AI systems to detect misogynistic content in multimodal data like memes, combining image and text analysis. Current research emphasizes multimodal learning approaches, often employing transformer-based architectures like UNITER, along with techniques such as multi-task learning and ensemble methods to improve accuracy and address the inherent subjectivity in identifying misogyny. This field is crucial for mitigating online harassment and hate speech targeting women, contributing to safer online environments and advancing research in both natural language processing and computer vision.