Improving Zero-Shot Models with Label Distribution Priors

Understand

Labeling large image datasets with attributes such as facial age or object type is tedious and sometimes infeasible.

• Supervised machine learning methods provide a highly accurate solution, but require manual labels which are often unavailable.
• Zero-shot models (e.g., CLIP) do not require manual labels but are not as accurate as supervised ones, particularly when the attribute is numeric.
• We propose a new approach, CLIPPR (CLIP with Priors), which adapts zero-shot models for regression and classification on unlabelled datasets.