Memes have emerged as a prominent medium for conveying public sentiment on sensitive health topics such as vaccination. Unlike conventional multimodal tasks, memes feature implicit stances, sarcastic nuances, and complex cross-modal interactions, posing significant challenges for accurate stance detection. This paper presents our approach for the VaxMeme Shared Task @EEUCA 2026, which aims to classify vaccine-related memes into three distinct classes: Vaccine-critical, Neutral, and Pro-vaccine. Building upon MemeCLIP, we systematically enhance our framework via task-specific adaptation, lightweight cross-modal fusion, noise-aware training, LLM-assisted semantic augmentation, and inference-stage optimization, ultimately ensembling multiple complementary variants for final predictions. Our ensemble method achieves a Macro-F1 score of 0.8494 on the official test set, securing first place and demonstrating the critical efficacy of noise-aware training and late-stage ensembling for robust stance identification.