We introduce text2fabric, a novel dataset that links free-text descriptions
to various fabric materials. The dataset comprises 15,000 natural language
descriptions associated to 3,000 corresponding images of fabric materials.
Traditionally, material descriptions come in the form of tags/keywords, which
limits their expressivity, induces pre-existing knowledge of the appropriate
vocabulary, and ultimately leads to a chopped description system. Therefore, we
study the use of free-text as a more appropriate way to describe material
appearance, taking the use case of fabrics as a common item that non-experts
may often deal with. Based on the analysis of the dataset, we identify a
compact lexicon, set of attributes and key structure that emerge from the
descriptions. This allows us to accurately understand how people describe
fabrics and draw directions for generalization to other types of materials. We
also show that our dataset enables specializing large vision-language models
such as CLIP, creating a meaningful latent space for fabric appearance, and
significantly improving applications such as fine-grained material retrieval
and automatic captioning.

我们介绍了 text2fabric，这是一个将自由文本描述与各种面料材料相链接的新型数据集。该数据集包括 15,000 个自然语言描述，与 3,000 个相应的面料材料图像相关联。通过分析数据集，我们确定了从描述中出现的紧凑词汇表、属性集和关键结构。这使我们能够准确理解人们如何描述面料，并为推广到其他类型材料绘制方向。我们还展示了我们的数据集使得像 CLIP 这样的大型视觉语言模型能够专门化，为面料外观创建有意义的潜在空间，并显著提高了细粒度材料检索和自动字幕等应用。