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

翻译：关于色彩命名系统在高效沟通与感知机制双重压力下演化的长期理论，已获得越来越多语言学研究的支持，包括对纳凡拉语四十年历时数据的分析。这启发我们探索机器学习能否通过优化由高层识别性能表征的沟通效率，演化和发现类似的色彩命名系统。为此，我们提出一种新颖的色彩量化Transformer——CQFormer，该方法在量化色彩空间的同时保持机器对量化图像的识别精度。给定RGB图像，注释分支将其映射为索引图，随后利用调色板生成量化图像；与此同时，调色板分支采用关键点检测方式在全局色彩空间中寻找调色板中的合适颜色。通过与色彩注释的交互，CQFormer能够在机器视觉精度与色彩感知结构（如已发现色彩系统中清晰且稳定的色彩分布）之间取得平衡。尤为有趣的是，我们观察到人工色彩系统与人类语言中基本色彩术语之间存在一致的演化模式。此外，我们的色彩量化方法提供了一种高效量化手段，在有效压缩图像存储的同时，仍能在分类、检测等高层识别任务中保持高性能。大量实验表明，该方法在极低比特率色彩条件下具有优异性能，展现出将其集成至量化网络以实现从图像到网络激活值量化的潜力。源代码可访问https://github.com/ryeocthiv/CQFormer获取。