With/in ✅

Increases detail representation and allows the model to leverage both low-level (texture) and high-level (semantic) information. 4. Deep Feature Factorization (DFF)

This approach combines features from different network layers or resolutions for richer representation. With/In

Highlights semantically matching regions across sets of images for tasks like co-localization. 5. Explainable AI (X-PERICL) with In-Context Learning Increases detail representation and allows the model to