Search Results for author:
Found 5 papers, 3 papers with code
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.
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness
Neural architectures based on attention such as vision transformers are revolutionizing image recognition.
Grid Saliency for Context Explanations of Semantic Segmentation
Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions.
