Results 1 to 10 of about 1,281,797 (301)
Metacognition and Learning, 2022 The font size effect is a metamemory illusion in which larger-font items produce higher judgments of learning (JOLs) but not better memory, relative to smaller-font items.
Minyu Chang
exaly +2 more sources
Multi-Content GAN for Few-Shot Font Style Transfer [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface.
Azadi, Samaneh +5 more
core +2 more sources
CF-Font: Content Fusion for Few-Shot Font Generation [PDF]
Computer Vision and Pattern Recognition, 2023 Content and style disentanglement is an effective way to achieve few-shot font generation. It allows to transfer the style of the font image in a source domain to the style defined with a few reference images in a target domain.
Chi-Yin Wang +5 more
semanticscholar +1 more source
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization [PDF]
AAAI Conference on Artificial Intelligence, 2023 Few-shot font generation is challenging, as it needs to capture the fine-grained stroke styles from a limited set of reference glyphs, and then transfer to other characters, which are expected to have similar styles.
Mingshuai Yao +4 more
semanticscholar +1 more source
DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation [PDF]
Computer Vision and Pattern Recognition, 2023 Automatic generation of fonts can be an important aid to typeface design. Many current approaches regard glyphs as pixelated images, which present artifacts when scaling and inevitable quality losses after vectorization.
Ying-Tian Liu +5 more
semanticscholar +1 more source
VecFusion: Vector Font Generation with Diffusion [PDF]
Computer Vision and Pattern Recognition, 2023 We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions.
Vikas Thamizharasan +7 more
semanticscholar +1 more source
Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator [PDF]
Computer Vision and Pattern Recognition, 2022 Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures. Typically, only a few samples can serve as the style/content reference (termed few-shot learning), which further increases ...
Yu Kong +6 more
semanticscholar +1 more source
Few-Shot Font Generation by Learning Fine-Grained Local Styles [PDF]
Computer Vision and Pattern Recognition, 2022 Few-shot font generation (FFG), which aims to generate a new font with a few examples, is gaining increasing attention due to the significant reduction in labor cost.
Licheng Tang +9 more
semanticscholar +1 more source
Diff-Font: Diffusion Model for Robust One-Shot Font Generation [PDF]
International Journal of Computer Vision, 2022 Font generation is a difficult and time-consuming task, especially in those languages using ideograms that have complicated structures with a large number of characters, such as Chinese.
Haibin He +6 more
semanticscholar +1 more source
DG-Font: Deformable Generative Networks for Unsupervised Font Generation [PDF]
Computer Vision and Pattern Recognition, 2021 Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years. However, existing methods for font generation are often in supervised learning.
Yangchen Xie +3 more
semanticscholar +1 more source