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cs.CV2026
Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness
Yehonatan Elisha, Oren Barkan, Noam Koenigstein
Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than semantically meaningful features…
cs.CV2025
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
Yehonatan Elisha, Seffi Cohen, Oren Barkan +1
Saliency maps are widely used for visual explanations in deep learning, but a fundamental lack of consensus persists regarding their intended purpose and alignment with diverse use…