The papers discussed highlight advancements in vision AI, focusing on improving the reliability and trustworthiness of computer vision models in various applications. The research explores ways to enhance explainability, interpretability, and robustness in image analysis, particularly in safety-critical domains such as healthcare and manufacturing. Key findings include the development of novel methods for reconstructing 3D faces under occlusion, improving medical image analysis with interactive models, creating a comprehensive benchmark for video anomaly detection in smart homes, and proposing multi-view industrial anomaly detection architectures using normalizing flows. These contributions aim to make vision AI more practical, trustworthy, and usable in real-world settings.