arXiv:2607. 13647v1 Announce Type: cross Abstract: Do vision models see colors the way humans do?
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The paper presents a data‑driven method for estimating perceptual color differences by training regression models on human similarity judgments of 2,000 color pairs. Using COLIBRI fuzzy linguistic categories as features, linear regression achieves an R² of 0.595, outperforming RGB and HSI representations. The best result, an R² of 0.703, is obtained with LightGBM on a combined representation, showing that graded perceptual categories improve color‑difference prediction.
arXiv:2609.09124v1 Announce Type: cross Abstract: Visual encoders construct a representation of the image input for Vision-Language models. How much conceptual, as opposed to immediately visible, inf...
arXiv:2608. 10195v1 Announce Type: cross Abstract: Human vision organizes what it sees into wholes: same-colored points group into series, similar marks cohere into categories, and shapes complete into recognizable objects.
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.
arXiv:2609.14495v1 Announce Type: new Abstract: Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently...
arXiv:2608.30964v1 Announce Type: new Abstract: Pretrained vision embeddings are increasingly used as general-purpose representations for modelling how people appraise urban scenes, and are validated...