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Found 5 papers, 4 papers with code
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models
We show that AutoCLIP outperforms baselines across a broad range of vision-language models, datasets, and prompt templates consistently and by up to 3 percent point accuracy.
Zero-Shot Visual Classification with Guided Cropping
We empirically show that our approach improves zero-shot classification results across architectures and datasets, favorably for small objects.
Multi-Attribute Open Set Recognition
While conventional OSR approaches can detect Out-of-Distribution (OOD) samples, they cannot provide explanations indicating which underlying visual attribute(s) (e. g., shape, color or background) cause a specific sample to be unknown.
Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain
Our approach extends the training set with an additional dataset (the source domain), which is specifically designed to facilitate learning independent representations of basic visual factors.
DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities
We also argue that it is necessary for DNNs to exploit GO to overcome shortcut learning.
