DeepAI AI Chat
Log In Sign Up

Grounding learning of modifier dynamics: An application to color naming

by   Xudong Han, et al.
The University of Melbourne

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.


page 1

page 2

page 3

page 4


Pragmatically Informative Color Generation by Grounding Contextual Modifiers

Grounding language in contextual information is crucial for fine-grained...

Learning Distributions of Meant Color

When a speaker says the name of a color, the color they picture is not n...

Generative Model Watermarking Based on Human Visual System

Intellectual property protection of deep neural networks is receiving at...

Calculation reduction method for color computer-generated hologram using color space conversion

We report a calculation reduction method for color computer-generated ho...

Fast Color Space Transformations Using Minimax Approximations

Color space transformations are frequently used in image processing, gra...

Illustrating Color Evolution and Color Blindness by the Decoding Model of Color Vision

A symmetrical model of color vision, the decoding model as a new version...