This study introduces DC-CLIP, a lightweight multilingual vision-language model trained via a novel multilingual vision-language knowledge distillation and alignment process. Framework Features: - High-quality Chinese and English text-image pairs for robust feature learning. - Utilizes a two-stage training approach focusing on feature distillation and alignment. Benefits and Results: - Demonstrates strong performance in zero-shot image classification for both English and Chinese contexts. - The model’s design allows for effective and efficient multilingual feature integration. Relevance and Future Work: - Possesses significant implications for the development of more inclusive and versatile AI technologies that transcend language barriers. Opinion: DC-CLIP is a groundbreaking framework that could revolutionize the use of AI in multilingual contexts, providing inclusivity and broader applicability across languages.