arXiv AI
Canonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs
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Canonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs
The paper investigates how vision encoders and Vision‑Language Models (VLMs) encode conceptual information by using canonical color as a test case. By creating a dataset of objects with canonical colors and probing encoders with both color and grayscale images, the authors show that canonical color can still be decoded from grayscale inputs and is linked to predicted object identity. They further demonstrate that post‑training of VLMs can significantly influence color decodability within the vision encoder, suggesting that canonical color is a useful tool for tracing conceptual semantics in these models.
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