  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.
