# Weakly Supervised Domain-Specific Color Naming Based on Attention

arXiv: https://arxiv.org/abs/1805.04385 · HTML: https://mlsanity.com/paper/1805.04385/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 3 of 3)

- [NamedCurves: Learned Image Enhancement via Color Naming](https://mlsanity.com/paper/2407.09892/) (2024)
- [Leveraging Color Naming for Image Enhancement](https://mlsanity.com/paper/2607.08185/) (2026)
- [Semantic Context Matters: Analysis of Color Names Across Domains](https://mlsanity.com/paper/2607.17221/) (2026)

## Abstract (arXiv)

The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.