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. We will release the source code soon.

翻译：长期以来的理论认为，颜色命名系统是在高效沟通与感知机制的双重压力下演化的，这一观点得到了越来越多语言学研究的支持，包括对纳法安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以高层识别性能为代表的高效沟通，演化并发现类似的颜色命名系统。在此，我们提出一种新颖的颜色量化Transformer——CQFormer，它在保持量化图像机器识别准确性的同时，对颜色空间进行量化。给定一张RGB图像，注释分支将其映射为索引图，随后通过调色板生成量化图像；同时，调色板分支采用关键点检测方法从整个颜色空间中为调色板寻找合适的颜色。通过与颜色注释的交互，CQFormer能够在机器视觉准确性与颜色感知结构（如所发现颜色系统中清晰且稳定的颜色分布）之间取得平衡。非常有趣的是，我们甚至观察到人工颜色系统与人类语言中的基本颜色词之间存在一致的演化模式。此外，我们的颜色量化方法还提供了一种高效的量化手段，在有效压缩图像存储的同时，保持分类、检测等高层识别任务的高性能。大量实验表明，该方法在极低比特率颜色下具有优异性能，展现出集成到从图像到网络激活的量化网络中的潜力。我们即将开源相关代码。