When deploying a machine learning model in production, it is common to encounter changes in the data distribution, such as shifts in covariates [Shimodaira, 2000], labels [Saerens et al., 2002,

Novel view synthesis of dynamic scenes is becoming important in various applications, including augmented and virtual reality.

This makes them overlook the many possible interactions across the input modalities and deems them unsafe for high-risk tasks requiring reliable uncertainty estimation.

We collect 312 programs from various sources, including daily programs from college homework, the international competition (SV -COMP), benchmarks from previous papers (SLING), and programs from real-world software systems (Linux Kernel, GlibC, LiteOS, and Zephyr).

The project code is availableathttps://github.com/mrirecon/aid.

Vision-Language Models seamlessly discriminate among arbitrary semantic categories, yet they still suffer from poor generalization when presented with challenging examples.