Metacognitive Artificial Intelligence in Vision Foundation Models: Research Challenges

The adoption of Vision Foundation Models (VFMs) in high stakes scenarios has spurred the demand for task specific, high performance models. However, the lack of explainability of VFMs makes it challenging to ensure safety, reliability, and resilience across tasks when facing data distributions different from those seen during training. Recently, approaches based on metacognition, the human ability to regulate cognitive processes, have emerged as a promising way to understand these large models. This paper surveys the interdisciplinary connection between metacognition and state of the art VFMs, and further examines its relationship with knowledge distillation (KD), a widely used technique in VFMs. The paper concludes by outlining possible avenues for future research on the topic.