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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.
A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)
Driven by the above, this work comprehensively investigates the safety objectives of CLIP models, specifically focusing on three key properties: resilience to visual factor variations, calibrated uncertainty estimations, and the ability to detect anomalous inputs.
A Bag-of-Prototypes Representation for Dataset-Level Applications
The former measures how suitable a training set is for a target domain, while the latter studies how challenging a test set is for a learned model.
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