Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer

TL;DR

This paper introduces CQFormer, a novel transformer-based colour quantisation method that discovers colour naming systems optimized for recognition efficiency, mirroring linguistic evolution patterns and improving image compression and recognition performance.

Contribution

We propose CQFormer, a transformer model that quantises colour space while balancing recognition accuracy and perceptual colour structure, revealing evolution patterns similar to human languages.

Findings

Discovered colour systems show patterns similar to human language colour terms.

Achieved high recognition accuracy with extremely low bit-rate colour quantisation.

Enhanced image compression while maintaining high-level recognition performance.

Abstract

The long-standing theory that a colour-naming system evolves under dual pressure of efficient communication and perceptual mechanism is supported by more and more linguistic studies, including analysing four decades of diachronic data from the Nafaanra language. This inspires us to explore whether machine learning could evolve and discover a similar colour-naming system via optimising the communication efficiency represented by high-level recognition performance. Here, we propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining the accuracy of machine recognition on the quantised images. Given an RGB image, Annotation Branch maps it into an index map before generating the quantised image with a colour palette; meanwhile the Palette Branch utilises a key-point detection way to find proper colours in the palette among the whole colour space. By interacting with colour annotation, CQFormer is able to balance both the machine vision accuracy and colour perceptual structure such as distinct and stable colour distribution for discovered colour system. Very interestingly, we even observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages. Besides, our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage while maintaining high performance in high-level recognition tasks such as classification and detection. Extensive experiments demonstrate the superior performance of our method with extremely low bit-rate colours, showing potential to integrate into quantisation network to quantities from image to network activation. The source code is available at https://github.com/ryeocthiv/CQFormer