NAME YOUR COLOUR FOR THE TASK: ARTIFICIALLY DISCOVER COLOUR NAMING VIA COLOUR QUANTI-SATION TRANSFORMER

Abstract

The long-standing theory that a colour-naming system evolves under the dual pressure of efficient communication and perceptual mechanism is supported by more and more linguistic studies including the analysis of four decades' diachronic data from the Nafaanra language. This inspires us to explore whether artificial intelligence 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 palette among 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 a 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. We will release the source code upon acceptance.

1. INTRODUCTION

Hath not a Jew eyes? Hath not a Jew hands, organs,dimensions, senses, affections, passions? William Shakespeare "The Merchant of Venice" Does artificial intelligence share the same perceptual mechanism for colours as human beings? We aim to explore this intriguing problem through AI simulation in this paper. Colour involves the visual reception and neural registering of light stimulants when the spectrum of light interacts with cone cells in the eyes. Physical specifications of colour also include the reflective properties of the physical objects, geometry incident illumination, etc. By defining a colour space (Forsyth & Ponce, 2002) , people could identify colours directly according to these quantifiable coordinates. Compared to the pure physiological nature of hue categorisation, the complex phenomenon of colour naming or colour categorisation spans multiple disciplines from cognitive science to anthropology. Solid diachronic research (Berlin & Kay, 1969 ) also suggests that human languages are constantly evolving to acquire new colour names, resulting in an increasingly fine-grained colour naming system. This evolutionary process is hypothesised to be under the pressure of both communication efficiency and perceptual structure. Communication efficiency requires shared colour partitioning to be communicated accurately with a lexicon as simple and economical as possible. Colour perceptual structure is relevant to human perception in nature. For example, the colour space distance 1

