Align Once to Explain: Feature Alignment for Scalable B-cosification of Foundational Vision Transformers
Paper recorded by Signals 4 on 2026-11-02 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03
Category: cs.CV · 计算机视觉 · first seen 2026-11-03
Abstract
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 approach that efficiently converts foundational vision models into inherently interpretable B-cos variants. Once aligned, the B-cos bac
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Cite this page: Align Once to Explain: Feature Alignment for Scalable B-cosification of Foundational Vision Transformers: the #27 most recent of 710 cs.CV papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/align-once-to-explain-feature-alignment-for-scalable-b-cosification-of-foundatio.html
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