The field of vision-language models is rapidly evolving, with a focus on improving robustness, adaptability, and generalization capabilities. Recent developments have emphasized the importance of leveraging semantic relationships between modalities, addressing error accumulation in unknown sample detection, and enhancing resilience against corruptions. Notably, innovative approaches have been proposed to expand zero-shot object counting, Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models, and Single Domain Generalization for Few-Shot Counting. Furthermore, benchmarking efforts, such as REOBench, have highlighted the vulnerability of current Earth observation foundation models to real-world corruptions, underscoring the need for more robust and reliable models. Noteworthy papers include: Open Set Domain Adaptation with Vision-language models via Gradient-aware Separation, which proposes a novel approach to open-set domain adaptation using Contrastive Language-Image Pretraining. Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models, which introduces AdvCLIP-LoRA, a method to enhance the adversarial robustness of CLIP models fine-tuned with LoRA in few-shot settings.