Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for crosslingual NER and can outperform multilingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we presResearch goal: To what extent do multimodal cross-lingual NER models (e.g., combining text and image embeddings) improve entity recognition robustness in low-resource languages compared to text-only models, as measured by accuracy on standard benchmarks like CoNLL-2003?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.