# Grounding learning of modifier dynamics: An application to color naming

arXiv: https://arxiv.org/abs/1909.07586 · HTML: https://mlsanity.com/paper/1909.07586/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Language](https://mlsanity.com/paper/2311.13105/) (2023)
- [Improving Object Detection and Attribute Recognition by Feature Entanglement Reduction](https://mlsanity.com/paper/2108.11501/) (2021)

## Abstract (arXiv)

Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as 'dirty blue'. We present a model of color modifiers that, compared with previous additive models in RGB space, learns more complex transformations. In addition, we present a model that operates in the HSV color space. We show that certain adjectives are better modeled in that space. To account for all modifiers, we train a hard ensemble model that selects a color space depending on the modifier color pair. Experimental results show significant and consistent improvements compared to the state-of-the-art baseline model.