The field of computer vision and machine learning is rapidly evolving, with a focus on improving the accuracy and reliability of models. Recent developments have highlighted the importance of feature upsampling methods for vision foundation models, which can significantly improve the quality of features and enable more accurate dense predictions. Additionally, there is a growing interest in explainable AI (XAI) frameworks that can help validate vision models and identify areas where they underperform. Interactive visual analytics tools are also being developed to analyze and understand the behavior of deep neural networks. Furthermore, automated vision-based assistance tools are being explored for medical applications, such as bronchoscopy, to improve diagnosis and monitoring procedures. Notable papers in this area include: VISLIX, an XAI framework for validating vision models, and ChannelExplorer, an interactive visual analytics tool for analyzing image-based outputs across model layers. Automated vision-based assistance tools, such as the pipeline for automated subglottic stenosis severity estimation, are also showing promise in medical applications.