@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},}
CVIU
Take a prior from other tasks for severe blur removal
Pei Wang, Yu Zhu, Danna Xue, Qingsen Yan, Jinqiu Sun, Sung-eui Yoon, and Yanning Zhang
@article{wang2023take,title={Take a prior from other tasks for severe blur removal},author={Wang, Pei and Zhu, Yu and Xue, Danna and Yan, Qingsen and Sun, Jinqiu and Yoon, Sung-eui and Zhang, Yanning},journal={Computer Vision and Image Understanding},pages={104027},year={2024},publisher={Elsevier},}
arXiv
Synth-to-Real Unsupervised Domain Adaptation for Instance Segmentation
Yachan Guo, Yi Xiao, Danna Xue, Jose Luis Gomez Zurita, and Antonio M López
@article{yachan2024synth,title={Synth-to-Real Unsupervised Domain Adaptation for Instance Segmentation},author={Guo, Yachan and Xiao, Yi and Xue, Danna and Zurita, Jose Luis Gomez and L{\'o}pez, Antonio M},journal={arXiv preprint arXiv:2405.09682},year={2024},}
2023
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},}
PR
Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance
Rui Li, Danna Xue, Shaolin Su, Xiantuo He, Qing Mao, Yu Zhu, Jinqiu Sun, and Yanning Zhang
@article{li2023learning,title={Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance},author={Li, Rui and Xue, Danna and Su, Shaolin and He, Xiantuo and Mao, Qing and Zhu, Yu and Sun, Jinqiu and Zhang, Yanning},journal={Pattern Recognition},volume={137},pages={109297},year={2023},publisher={Elsevier},}
2022
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},}
IEEE-TMM
Self-supervised monocular depth estimation with frequency-based recurrent refinement
Rui Li, Danna Xue, Yu Zhu, Hao Wu, Jinqiu Sun, and Yanning Zhang
@article{li2022self,title={Self-supervised monocular depth estimation with frequency-based recurrent refinement},author={Li, Rui and Xue, Danna and Zhu, Yu and Wu, Hao and Sun, Jinqiu and Zhang, Yanning},journal={IEEE Transactions on Multimedia},volume={25},pages={5626--5637},year={2022},publisher={IEEE},}
2020
MTAP
Dim small target detection based on convolutinal neural network in star image
Danna Xue, Jinqiu Sun, Yaoqi Hu, Yushu Zheng, Yu Zhu, and Yanning Zhang
@article{xue2020dim,title={Dim small target detection based on convolutinal neural network in star image},author={Xue, Danna and Sun, Jinqiu and Hu, Yaoqi and Zheng, Yushu and Zhu, Yu and Zhang, Yanning},journal={Multimedia Tools and Applications},volume={79},pages={4681--4698},year={2020},publisher={Springer},}
Dim small target detection based on convolutinal neural network in star imageMultimedia Tools and Applications, 2020