Image classification under strict time constraints requires a delicate balance between feature complexity and computational overhead. This paper presents an optimized ensemble methodology developed for the NAKABA competition, focusing on identifying structural destruction. We propose a hybrid architecture that leverages two distinct Convolutional Neural Networks (EfficientNetB0 and EfficientNetB3) as base feature extractors, coupled with a Support Vector Machine (SVM) functioning as a meta-classifier. Instead of standard probability averaging or processing high-dimensional embeddings directly, the Meta-SVM acts as a learned gating mechanism to optimally combine the low-dimensional probability predictions of the base models. This ensures robust performance without the latency of heavier deep learning architectures. Empirical results demonstrate the efficacy of this approach. The model achieved a validation accuracy of 0.884 and a weighted F1-score of 0.885, with a notable F1-score of 0.839 on the challenging ’destruction’ class. On the official NAKABA leaderboard test set, the ensemble maintained strong generalization, achieving an F1-score of 0.831 and an accuracy of 0.845, which secured the 12th position overall and proved the model’s high effectiveness within the competition’s strict operational constraints.

Internet memes have become a dominant and highly accessible medium for political discourse on social media. However, their multimodal nature—combining culturally specific visual symbols with code-mixed text—presents a significant challenge for automated content analysis, particularly in low-resource languages. In this study, we describe the system submitted by team RMS for the Multi-Level Political Meme Classification shared task at DravidianLangTech @ ACL 2026, focusing exclusively on the Tamil language track. We propose a robust late-fusion multimodal architecture that leverages a pre-trained ResNet-50 network for visual feature extraction and a Transformer-based model (MuRIL) for processing code-mixed Tamil text. The modalities are aligned using bidirectional cross-modal attention and combined using a Gated Multimodal Unit, allowing the model to dynamically weight the importance of visual versus textual cues. Our system ranked 11th on the official leaderboard with a macro-averaged F1-score of 0.7382. Through detailed error analysis, we demonstrate that while our gated fusion approach excels at identifying explicit trolling stances, it struggles with complex target resolution when visual and textual cues contradict.