Political memes are inherently multimodal, combining visual cues, embedded text, and code-mixed language to express political stance and target specific political entities. This makes automated analysis particularly challenging in low-resource settings such as Tamil. We present the KEC system for the MMLow 2026 shared task at AACL-IJCNLP 2026, which requires hierarchical classification of Tamil political memes at two levels: stance identification and political target identification. Our approach combines XLM-RoBERTa and Vision Transformer representations with OCR-derived textual information, multimodal fusion, and bidirectional cross-modal attention. We further introduce hierarchy-aware prediction in which Level-2 target identification is conditioned on the Level-1 stance distribution, together with class-weighted objectives, adaptive task weighting, and hierarchical consistency regularization. A controlled six-stage ablation study and three-seed stability analysis are conducted on the validation split. The final hierarchical model obtains a Level-1 macro-F1 of 78.77 ± 1.21% and Level-2 macro-F1 of 46.07 ± 3.23%, with a joint exact-match accuracy of 55.40 ± 2.34% across seeds 42, 123, and 2024. Our analysis indicates that visual information is particularly beneficial for stance classification, whereas multimodal fusion plays an important role in target identification, while OCR augmentation and hierarchical conditioning introduce performance trade-offs at Level 2.