I am currently a postdoctoral researcher in Computer Vision Center (CVC), Barcelona, Spain. I received my Ph.D. degree in July 2024 from Universitat Autònoma de Barcelona (UAB) and Northwestern Polytechnical University (NPU), co-supervised by Professor Yanning Zhang, Assoc. Prof. Javier Vazquez Corral and Dr. Luis Herranz.
My research focuses on image interpretation and editing, particularly image and video segmentation and color manipulation. My work aims to develop advanced techniques for analyzing and modifying visual content, contributing to applications in computer vision and digital media.
@article{xue2024palette,title={Palette-based Color Harmonization via Color Naming},author={Xue, Danna and Vazquez-Corral, Javier and Herranz, Luis and Zhang, Yanning and Brown, Michael S.},journal={IEEE Signal Processing Letters},year={2024},publisher={IEEE},}
CGF
Integrating high-level features for consistent palette-based multi-image recoloring
Danna Xue, Javier Vazquez-Corral, Luis Herranz, Yanning Zhang, and Michael S. Brown
@inproceedings{xue2023integrating,title={Integrating high-level features for consistent palette-based multi-image recoloring},author={Xue, Danna and Vazquez-Corral, Javier and Herranz, Luis and Zhang, Yanning and Brown, Michael S.},booktitle={Computer Graphics Forum},pages={e14964},year={2023},organization={Wiley Online Library},}
ICASSP’23
Burst perception-distortion tradeoff: analysis and evaluation
Danna Xue, Luis Herranz, Javier Corral, and Yanning Zhang
In IEEE International Conference on Acoustics, Speech and Signal Processing, 2023
@inproceedings{xue2023burst,title={Burst perception-distortion tradeoff: analysis and evaluation},author={Xue, Danna and Herranz, Luis and Corral, Vazquez-Corral, Javier and Zhang, Yanning},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing},pages={1--5},year={2023},organization={IEEE},}
ACM MM’22
Slimseg: Slimmable semantic segmentation with boundary supervision
Danna Xue, Fei Yang, Pei Wang, Luis Herranz, Jinqiu Sun, Yu Zhu, and Yanning Zhang
In ACM International Conference on Multimedia, 2022
Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the desired accuracy-efficiency tradeoff. More specifically, we employ parametrized channel slimming by stepwise downward knowledge distillation during training. Motivated by the observation that the differences between segmentation results of each submodel are mainly near the semantic borders, we introduce an additional boundary guided semantic segmentation loss to further improve the performance of each submodel. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance than independent models. Extensive experiments on semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the generalization ability of our framework.
@inproceedings{xue2022slimseg,title={Slimseg: Slimmable semantic segmentation with boundary supervision},author={Xue, Danna and Yang, Fei and Wang, Pei and Herranz, Luis and Sun, Jinqiu and Zhu, Yu and Zhang, Yanning},booktitle={ACM International Conference on Multimedia},pages={6539--6548},year={2022},}
! Dobby is a free Elf! (only for this weekend…) 
