Improving Zero-Shot Models with Label Distribution Priors

• URL: http://arxiv.org/abs/2212.00784v1
• Date: Thu, 1 Dec 2022 18:59:03 GMT
• Title: Improving Zero-Shot Models with Label Distribution Priors
• Authors: Jonathan Kahana, Niv Cohen, Yedid Hoshen
• Abstract summary: We propose a new approach, CLIPPR, which adapts zero-shot models for regression and classification on unlabelled datasets. We demonstrate an improvement of 28% in mean absolute error on the UTK age regression task. We also present promising results for classification benchmarks, improving the classification accuracy on the ImageNet dataset by 2.83%, without using any labels.
• Score: 33.51714665243138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Our method does not use any annotated images. Instead, we assume a prior over the label distribution in the dataset. We then train an adapter network on top of CLIP under two competing objectives: i) minimal change of predictions from the original CLIP model ii) minimal distance between predicted and prior distribution of labels. Additionally, we present a novel approach for selecting prompts for Vision & Language models using a distributional prior. Our method is effective and presents a significant improvement over the original model. We demonstrate an improvement of 28% in mean absolute error on the UTK age regression task. We also present promising results for classification benchmarks, improving the classification accuracy on the ImageNet dataset by 2.83%, without using any labels.

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