Closed moonfoam closed 1 year ago
Thanks for open-sourcing this inspiring work!
I am now doing some comparative experiments for this work. The visualization of the training process seems to work fine. However, the output of the test is not so good. I guess the reason may be that the components of reference characters do not cover the test set well. (since the number of all our reference characters is 16)
So I wonder if the choice of reference characters and the coverage of the components have a big impact on the results?
Yes, the content-reference mapping is significant in experiments. Could u give some examples of your cr_mapping dict?
Thanks for your quick reply.
We discovered this while doing one-shot and few-shot comparison (around fifteen characters in total) experiments . But since the reference character set is randomly selected and fixed, many components are actually not covered, which may be unfair to this method. By the way, the component-wise spatial correspondence in this paper is well designed and inspired me a lot.
Thanks for your quick reply.
We discovered this while doing one-shot and few-shot comparison (around fifteen characters in total) experiments . But since the reference character set is randomly selected and fixed, many components are actually not covered, which may be unfair to this method. By the way, the component-wise spatial correspondence in this paper is well designed and inspired me a lot.
Thats right, Fsfont highly depends on the reference selection and was designed in component-wise, so one-shot task is not suitable for it. Actually there are already some works introducing stroke-wise attention in FFG. Hope it could help you : )
Thanks for your quick reply. We discovered this while doing one-shot and few-shot comparison (around fifteen characters in total) experiments . But since the reference character set is randomly selected and fixed, many components are actually not covered, which may be unfair to this method. By the way, the component-wise spatial correspondence in this paper is well designed and inspired me a lot.
Thats right, Fsfont highly depends on the reference selection and was designed in component-wise, so one-shot task is not suitable for it. Actually there are already some works introducing stroke-wise attention in FFG. Hope it could help you : )
Thank you @tlc121 for the reply.
感谢您的快速回复。 我们在做一次和少量比较(总共大约十五个字符)实验时发现了这一点。但是由于参考字符集是随机选择和固定的,所以很多组件实际上并没有被覆盖,这可能对这种方法不公平。顺便说一句,本文中的组件空间对应关系设计得很好,给了我很大的启发。
没错,Fsfont 高度依赖于参考选择,并且是按组件设计的,因此一次性任务不适合它。实际上已经有一些作品在 FFG 中引入了 stroke-wise attention。希望它能帮助你:)
您好,请问您能列举一些stroke-wise attention的相关文献吗?
感谢您的快速回复。 我们在做一次和少量比较(总共大约十五个字符)实验时发现了这一点。但是由于参考字符集是随机选择和固定的,所以很多组件实际上并没有被覆盖,这可能对这种方法不公平。顺便说一句,本文中的组件空间对应关系设计得很好,给了我很大的启发。
没错,Fsfont 高度依赖于参考选择,并且是按组件设计的,因此一次性任务不适合它。实际上已经有一些作品在 FFG 中引入了 stroke-wise attention。希望它能帮助你:)
您好,请问您能列举一些stroke-wise attention的相关文献吗?
字节的xmpfont就是stroke-wise的,还有一篇华为的文章,具体名字有点忘了,您可以自行搜索下
Thanks for open-sourcing this inspiring work!
I am now doing some comparative experiments for this work. The visualization of the training process seems to work fine. However, the output of the test is not so good. I guess the reason may be that the components of reference characters do not cover the test set well. (since the number of all our reference characters is 16)
So I wonder if the choice of reference characters and the coverage of the components have a big impact on the results?