The recent advancements in Vision-Language Models (VLM) have significantly pushed the boundaries of image classification and retrieval, particularly in low-resource and few-class domains. Innovations in retrieval-based strategies and the integration of dense neural networks with efficient indexing systems have shown remarkable improvements in classification accuracy and retrieval speed. Notably, the field is witnessing a shift towards more inclusive benchmarks that incorporate diverse languages and cultural perspectives, addressing the need for multilinguality in vision-language tasks. Additionally, there is a growing emphasis on mitigating biases within VLMs, with novel approaches focusing on fine-grained debiasing techniques that adapt to individual inputs rather than applying a uniform correction. These developments collectively indicate a trend towards more robust, efficient, and culturally sensitive VLM applications, with a particular focus on enhancing performance in niche and underrepresented domains.