Internet memes serve as powerful vehicles of expression across platforms like Instagram, Twitter, and WhatsApp. However, they often carry implicit messages such as humor, sarcasm, or offense especially in the context of marginalized communities. Understanding such intent is crucial for effective moderation and content filtering. This paper introduces a deep learning-based multimodal framework developed for the CASE 2025 Shared Task on detecting hate, humor, and stance in memes related to marginalized movements. The study explores three architectures combining textual models (BERT, XLM-RoBERTa) with visual encoders (ViT, CLIP), enhanced through cross-modal attention and Transformer-based fusion. Evaluated on four subtasks, the models effectively classify meme content—such as satire and offense—demonstrating the value of attention-driven multimodal integration in interpreting nuanced social media expressions