Abstract
Human vision can distinguish between a vast spectrum of colours, estimated to be between 2 to 7 million discernible shades. However, this impressive range does not inherently imply that all these colours have been precisely named and described within our lexicon. We often associate colours with familiar objects and concepts in our daily lives. This research endeavors to bridge the gap between our visual perception of countless shades and our ability to articulate and name them accurately. A novel model has been developed to achieve this goal, leveraging Bidirectional Long Short-Term Memory (BiLSTM) networks with Active learning. This model operates on a proprietary dataset meticulously curated for this study. The primary objective of this research is to create a versatile tool for categorizing and naming previously unnamed colours or identifying intermediate shades that elude traditional colour terminology. The findings underscore the potential of this innovative approach in revolutionizing our understanding of colour perception and language. Through rigorous experimentation and analysis, this study illuminates a promising avenue for Natural Language Processing (NLP) applications in diverse industries. By facilitating the exploration of the vast colour spectrum the potential applications of NLP are extended beyond conventional boundaries.
Abstract (translated)
人类视觉可以区分出数百万种色彩的广泛范围，据估计在2到7百万个可辨别色调之间。然而，这一令人印象深刻的范围并不暗示所有这些颜色都已在我们的词汇库中准确地命名和描述。我们通常将颜色与我们在日常生活中熟悉的物体和概念相关联。这项研究旨在弥合我们视觉感知到无数种色彩与我们准确表达和命名它们的能力之间的差距。为了实现这一目标，利用双向长短时记忆（BiLSTM）网络与主动学习，开发了一个新模型。该模型在专门为这项研究 curated的私用数据集上运行。这一研究的主要目标是为分类和命名以前未命名的颜色或识别中间色调提供一个实用的工具。研究结果强调了这种创新方法在颠覆我们对色彩感知和语言的理解方面的潜力。通过严格的实验和分析，这项研究揭示了自然语言处理（NLP）在各种行业应用中的一个有前景的途径。通过促进对丰富色彩范围的探索，NLP的应用范围超越了传统边界。
URL
https://arxiv.org/abs/2311.06542
