The field of out-of-distribution (OOD) detection is rapidly advancing, with a focus on developing innovative methods to improve the reliability and trustworthiness of machine learning models. Recent developments have seen a shift towards leveraging multimodal representations, visual-contextual information, and statistical nonparametric tests to detect OOD objects and samples. These approaches aim to address the challenges of domain shift, limited scalability, and lack of interpretability in existing OOD detection methods. Notable papers in this area include RUNA, which proposes a novel framework for object-level OOD detection using regional uncertainty alignment, and STOOD-X, a two-stage methodology that combines statistical nonparametric testing with explainability enhancements. Additionally, methods such as EZ-AVOOD and VisTa have demonstrated superior performance in audio-visual and zero-shot object-level OOD detection, respectively. These advancements have the potential to significantly improve the robustness and reliability of machine learning models in real-world applications.