@inproceedings{zhao-etal-2024-mccstn,
    title = "{M}cc{STN}: Multi-Scale Contrast and Fine-Grained Feature Fusion Networks for Subject-driven Style Transfer",
    author = "Zhao, Honggang  and
      Xiao, Chunling  and
      Yang, Jiayi  and
      Jin, Guozhu  and
      Li, Mingyong",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.967",
    pages = "11090--11100",
    abstract = "Stylistic transformation of artistic images is an important part of the current image processing field. In order to access the aesthetic artistic expression of style images, recent research has applied attention mechanisms to the field of style transfer. This approach transforms style images into tokens by calculating attention and then migrating the artistic style of the image through a decoder. Due to the very low semantic similarity between the original image and the style image, this results in many fine-grained style features being discarded. This can lead to discordant artifacts or obvious artifacts. To address this problem, we propose MccSTN, a novel style representation and transfer framework that can be adapted to existing arbitrary image style transfers. Specifically, we first introduce a feature fusion module (Mccformer) to fuse aesthetic features in style images with fine-grained features in content images. Feature maps are obtained through Mccformer. The feature map is then fed into the decoder to get the image we want. In order to lighten the model and train it quickly, we consider the relationship between specific styles and the overall style distribution. We introduce a multi-scale augmented contrast module that learns style representations from a large number of image pairs.",
}

Stylistic transformation of artistic images is an important part of the current image processing field. In order to access the aesthetic artistic expression of style images, recent research has applied attention mechanisms to the field of style transfer. This approach transforms style images into tokens by calculating attention and then migrating the artistic style of the image through a decoder. Due to the very low semantic similarity between the original image and the style image, this results in many fine-grained style features being discarded. This can lead to discordant artifacts or obvious artifacts. To address this problem, we propose MccSTN, a novel style representation and transfer framework that can be adapted to existing arbitrary image style transfers. Specifically, we first introduce a feature fusion module (Mccformer) to fuse aesthetic features in style images with fine-grained features in content images. Feature maps are obtained through Mccformer. The feature map is then fed into the decoder to get the image we want. In order to lighten the model and train it quickly, we consider the relationship between specific styles and the overall style distribution. We introduce a multi-scale augmented contrast module that learns style representations from a large number of image pairs.
Zhao, Honggang Xiao, Chunling Yang, Jiayi Jin, Guozhu Li, Mingyong
MccSTN: Multi-Scale Contrast and Fine-Grained Feature Fusion Networks for Subject-driven Style Transfer