You can now explain any vision model's decisions using concepts without needing model internals or expensive labeled data—just show the model what happens when you hide concept regions.
Hide and Seek is a method for explaining black-box vision models using human-understandable concepts. Unlike existing approaches, it doesn't require access to model weights or labeled concept datasets. The method finds concept-relevant images, masks the concept regions, and measures how the model's output changes to determine what concepts influence predictions.