Raphael Maser

Align Once to Explain: Feature Alignment for Scalable B-cosification of Foundational Vision Transformers

Foundational vision models have become the de facto standard for many vision tasks due to their strong performance. However, they are notoriously opaque and remain hard to interpret. We present ALOE (ALign Once to Explain), a one-time, label-free feature alignment based approach that efficiently con