Enabling Calibration In The Zero-Shot Inference of Large Vision-Language Models

Will LeVine, Benjamin Pikus, Pranav Vishnu Raja, Fernando Amat

Keywords: Miscalibration, vision-language models, zero-shot

TL;DR: We perform an extensive investigation to show CLIP models are miscalibrated as a zero-shot model across relevant variables, and propose a calibration method that is aligned with CLIP's zero-shot usage.

Abstract: Calibration of deep learning models is crucial to their trustworthiness and safe usage, and as such, has been extensively studied in supervised classification models, with methods crafted to decrease miscalibration. However, there has yet to be a comprehensive study of the calibration of vision-language models that are used for zero-shot inference, like CLIP. We measure calibration across relevant variables like prompt, dataset, and architecture, and find that zero-shot inference with CLIP is miscalibrated. Furthermore, we propose a modified version of temperature scaling that is aligned with the common use cases of CLIP as a zero-shot inference model, and show that a single learned temperature generalizes for each specific CLIP model (defined by a chosen pre-training dataset and architecture) across inference dataset and prompt choice.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/enabling-calibration-in-the-zero-shot/code)