1. Technical novelty is limited. The proposed framework is essentially a transformer variant. Although this work is probably the first to apply a Transformer-like model on few-shot font generation, there exist works that have attempted to apply in closely related tasks, like style transfer [1]. 2. Evaluation is insufficient. For quantitative comparison, only IoU and classification accuracy are provided. It will be more convincing to provide comparison results on FID and SSIM to show the effectiveness of the proposed framework. 3. References and baseline methods are missing. [2] and [3] are both proposed for font generation but are not mentioned. 4. Experimental details are not clear. (1) how to conduct the classification task on content, as mentioned in Section 4.4? 2. As mentioned in Section 3.3.3, the discriminators are conditional. What is the input of the discriminators? 5. More insights are needed. It would be better to move the discussion in the Appendix to the main body of the manuscript.
References: [1] StyleFormer: Real-time Arbitrary Style Transfer via Parametric Style Composition, ICCV 2021 [2] Multi-Content GAN for Few-Shot Font Style Transfer, CVPR 2018 [3] DG-Font: Deformable Generative Networks for Unsupervised Font Generation, CVPR 2021
4. Experimental details are not clear. (1) how to conduct the classification task on content, as mentioned in Section 4.4?