Multimodal Knowledge Graphs (MMKGs) provide structured multimodal grounding for Multimodal Large Language Models (MLLMs), while Multimodal Entity Alignment (MMEA) is essential for integrating heterogeneous MMKGs. However, existing MMEA methods remain vulnerable to noisy modalities and unreliable pseudo-supervision. We propose CRMEA, a large–small collaborative framework for unsupervised MMEA that unifies PTM-based retrieval and MLLM-based reasoning through a closed-loop optimization process. CRMEA introduces a text-centric adaptive cross-modal coordination mechanism to regulate noisy visual evidence, together with a retrieve-and-vote strategy for reliable pseudo-supervision. Extensive experiments show that CRMEA consistently outperforms existing unsupervised MMEA methods and achieves performance competitive with state-of-the-art supervised approaches, while maintaining strong robustness, adaptability across heterogeneous backbones, and substantially improved cost-effectiveness over exhaustive MLLM-based alignment. Code and data are available at https://github.com/nsai-szu/CRMEA.