论文标题
字体形状到印象翻译
Font Shape-to-Impression Translation
论文作者
论文摘要
不同的字体具有不同的印象，例如优雅，恐怖和凉爽。本文根据变压器结构对基于零件的形状印象分析进行了处理，该分析能够通过其自我发项机制来处理本地部分之间的相关性。这种能力将揭示当地部分组合如何实现字体的特定印象。变压器的多功能性使我们能够实现两种非常不同的分析方法，即多标签分类和翻译。定量评估表明，我们基于变压器的方法比其他方法更准确地估计了一组本地部分的字体印象。然后，定性评估表明特定印象的重要本地部分。
Different fonts have different impressions, such as elegant, scary, and cool. This paper tackles part-based shape-impression analysis based on the Transformer architecture, which is able to handle the correlation among local parts by its self-attention mechanism. This ability will reveal how combinations of local parts realize a specific impression of a font. The versatility of Transformer allows us to realize two very different approaches for the analysis, i.e., multi-label classification and translation. A quantitative evaluation shows that our Transformer-based approaches estimate the font impressions from a set of local parts more accurately than other approaches. A qualitative evaluation then indicates the important local parts for a specific impression.
