论文标题
为任务命名您的颜色：通过颜色定量变压器人为地发现颜色命名
Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer
论文作者
论文摘要
越来越多的语言学研究支持了颜色命名系统在有效交流和感知机制的双重压力下演变的长期理论，包括分析Nafaanra语言的四十年的历时数据。这激发了我们探索机器学习是否可以通过优化高级识别性能代表的沟通效率来发展并发现类似的颜色命名系统。在这里，我们提出了一种新颖的颜色量化变压器CQFormer，该变压器可以量化颜色空间，同时保持机器识别在量化图像上的准确性。给定RGB图像，注释分支将其映射到索引图中，然后再用调色板生成定量图像。同时，调色板分支利用一种关键点检测方式来在整个颜色空间之间的调色板中找到合适的颜色。通过与颜色注释相互作用，CQFormer能够平衡机器视觉精度和颜色感知结构，例如发现的颜色系统的独特和稳定的颜色分布。有趣的是，我们甚至观察到人造颜色系统与跨人类语言的基本颜色术语之间的一致演变模式。此外，我们的颜色定量方法还提供了一种有效的定量方法，该方法可以有效地压缩图像存储，同时在高级识别任务（例如分类和检测）中保持高性能。广泛的实验证明了我们的方法具有极低的位量颜色的出色性能，表明将整合到定量网络到从图像到网络激活的数量的潜力。源代码可从https://github.com/ryeocthiv/cqformer获得
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
