Fetching the paper…

Reading the bibliography…

2024

Regional Style and Color Transfer

Ding, Zhicheng, Li, Panfeng, Yang, Qikai et al.

Original
PDF
Post on X

Understand

This paper presents a novel contribution to the field of regional style transfer.

Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to image with foreground elements such as person figures.
To address this limitation, we propose a new approach that leverages a segmentation network to precisely isolate foreground objects within the input image.
Subsequently, style transfer is applied exclusively to the background region.

Regional Style and Color Transfer · Around

Built on

E. Reinhard, M. Adhikhmin, B. Gooch, and P. Shirley, “Color transfer between images,”
2001
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results,” http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
2012
Earlier work this paper cites.
L. Engstrom, “Fast style transfer,”
2016
Earlier work this paper cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in
2017
Earlier work this paper cites.

Similar

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in
2018
Cited alongside, same era.
H. Peng, S. Huang, T. Zhou, Y. Luo, C. Wang, Z. Wang, J. Zhao, X. Xie, A. Li, T. Geng
2023
Cited alongside, same era.
J. Yao, C. Li, K. Sun, Y. Cai, H. Li, W. Ouyang, and H. Li, “Ndc-scene: Boost monocular 3d semantic scene completion in normalized devicecoordinates space,” in
2023
Cited alongside, same era.
L. Li, “Cpseg: Finer-grained image semantic segmentation via chain-of-thought language prompting,” in
2024
Cited alongside, same era.

Then

C.-H. Chang, X. Wang, and C. C. Yang, “Explainable ai for fair sepsis mortality predictive model,”
Original
2024
Closest in time.
Y. Xin, S. Luo, H. Zhou, J. Du, X. Liu, Y. Fan, Q. Li, and Y. Du, “Parameter-efficient fine-tuning for pre-trained vision models: A survey,”
Original
2024
Closest in time.
T. Deng, Y. Zhou, W. Wu, M. Li, J. Huang, S. Liu, Y. Song, H. Zuo, Y. Wang, Y. Yue
Original
2024
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…