Vision encoders construct the image representations used by vision-language models. How much conceptual information, rather than immediately visible image evidence, do these representations contain? We use canonical color as a controlled test case: can a vision encoder represent the typical color of an object concept, such as yellow for bananas, when visible color is removed from the input image? We construct a dataset of objects with canonical color labels and probe vision encoders for both color and object identity using RGB and grayscale inputs. We find that canonical color remains decodable from grayscale images and is tied to predicted object identity, suggesting a conceptual link between object and color information. Extending this analysis to full VLMs, we find that VLM post-training can have a surprisingly large effect on color decodability in the vision encoder. Overall, canonical color provides a controllable lens for tracing object-level semantic information in vision encoders and VLMs.

As interest grows in generating long, detailed image captions, standard evaluation metrics become increasingly unreliable. N-gram-based metrics though efficient, fail to capture semantic correctness. Representational Similarity (RS) metrics, designed to address this, initially saw limited use due to high computational costs, while today, despite advances in hardware, they remain unpopular due to low correlation to human judgments. Meanwhile, metrics based on large language models (LLMs) show strong correlation with human judgments, but remain too expensive for iterative use during model development.We introduce SPECS (Specificity-Enhanced CLIPScore), a reference-free RS metric tailored to long image captioning. SPECS modifies CLIP with a new objective that emphasizes specificity: rewarding correct details and penalizing incorrect ones. We show that SPECS matches the performance of open-source LLM-based metrics in correlation to human judgments, while being far more efficient. This makes it a practical alternative for iterative checkpoint evaluation during image captioning model development.Our code can be found at https://github.com/mbzuai-nlp/SPECS.