Vaccine stance detection in multimodal memes has emerged as an important yet challenging task, requiring models to interpret both textual and visual cues that jointly convey opinions. The difficulty lies in capturing subtle semantic interactions and handling class imbalance across stance categories. In this paper, we present our system developed for the VaxMeme 2026 Shared Task at EEUCA 2026. Our approach leverages a soft-voting ensemble of complementary models, combining DeBERTa-v3-large and RoBERTa-large for rich textual representation with CLIP (ViT-B/32) for joint vision-language understanding. We incorporate domain-specific preprocessing, techniques such as random token deletion, image enhancement, and balanced class oversampling to address dataset limitations. Through extensive ablation studies, we identify balanced class oversampling as the most effective component, significantly improving performance across models. Our final system achieves a macro F1-score of 0.8306, securing 8th place among 25 teams, demonstrating the effectiveness of ensemble-based multimodal learning for stance detection.