An Empirical Study Into What Matters for Calibrating Vision-Language Models

Vision--Language Models (VLMs) have emerged as the dominant approach for zero-shot recognition, adept at handling diverse scenarios and significant distribution changes. However, their deployment in risk-sensitive areas requires a deeper understanding of their uncertainty estimation capabilities, a relatively uncharted area. In this study, we explore the calibration properties of VLMs across different architectures, datasets, and training strategies. In particular, we analyze the uncertainty estimation performance of VLMs when calibrated in one domain, label set or hierarchy level, and tested in …

adept capabilities cs.cv cs.lg deployment distribution diverse explore language language models recognition risk study uncertainty understanding vision vision-language models vlms zero-shot