1 code implementation • 22 Mar 2024 • Wenlve Zhou, Zhiheng Zhou, Tianlei Wang, Delu Zeng
Typically, various prevailing methods baseline rely on constructing intermediate domains via cross-domain mixed sampling techniques to mitigate the performance decline caused by domain gaps.
Ranked #9 on Domain Adaptation on SYNTHIA-to-Cityscapes
no code implementations • 9 Dec 2023 • Jian Xu, Delu Zeng
However, despite Student-t Processes having a similar computational complexity as Gaussian Processes, there has been limited emphasis on the sparse representation of this model.
no code implementations • 22 Sep 2023 • Jian Xu, Shian Du, Junmei Yang, Qianli Ma, Delu Zeng
Furthermore, our method guarantees theoretically controlled prediction error for DGP models and demonstrates remarkable performance on various datasets.
no code implementations • 17 Sep 2023 • Jian Xu, Shian Du, Junmei Yang, Xinghao Ding, John Paisley, Delu Zeng
Bayesian inference for these models has been extensively studied and applied in tasks such as time series prediction.
no code implementations • 15 Aug 2023 • Shigui Li, Wei Chen, Delu Zeng
Based on the RD method and the truncated Taylor expansion of score-integrand, we propose SciRE-Solver with the convergence order guarantee for accelerating sampling of DMs.
Ranked #10 on Image Generation on CIFAR-10
1 code implementation • 2 Jul 2022 • Lei Cai, Yuli Fu, Wanliang Huo, Youjun Xiang, Tao Zhu, Ying Zhang, Huanqiang Zeng, Delu Zeng
The proposed method formulates a new multi-scale attention search space with multiple flexible modules that are favorite to the image de-raining task.
1 code implementation • CVPR 2022 • Shian Du, Yihong Luo, Wei Chen, Jian Xu, Delu Zeng
In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training.
no code implementations • 1 Nov 2021 • Huangxing Lin, Yihong Zhuang, Delu Zeng, Yue Huang, Xinghao Ding, John Paisley
Specifically, we treat the output of the network as a ``prior'' that we denoise again after ``re-noising''.
1 code implementation • 18 Sep 2021 • Liwen Zou, XinHang Luo, Delu Zeng, Liming Ling, Li-Chen Zhao
Weak Gaussian perturbations on a plane wave background could trigger lots of rogue waves, due to modulational instability.
no code implementations • 26 Jan 2021 • Delu Zeng, Minyu Liao, Mohammad Tavakolian, Yulan Guo, Bolei Zhou, Dewen Hu, Matti Pietikäinen, Li Liu
Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision.
1 code implementation • 8 Jun 2019 • Yixuan He, Tianyi Hu, Delu Zeng
Experimental results show that the proposed algorithm can generate precise masks that allow for various machine learning tasks such as supervised training.
Graphics
1 code implementation • 15 May 2018 • Delu Zeng, Yixuan He, Li Liu, Zhihong Chen, Jiabin Huang, Jie Chen, John Paisley
In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to deal with saliency detection with the tolerance of distortion.
no code implementations • CVPR 2017 • Xueyang Fu, Jia-Bin Huang, Delu Zeng, Yue Huang, Xinghao Ding, John Paisley
We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN).
no code implementations • CVPR 2016 • Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, Xinghao Ding
We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image.
no code implementations • 17 Mar 2016 • Tong Zhao, Lin Li, Xinghao Ding, Yue Huang, Delu Zeng
In this letter, an effective image saliency detection method is proposed by constructing some novel spaces to model the background and redefine the distance of the salient patches away from the background.
no code implementations • ICCV 2015 • Yiyong Jiang, Xinghao Ding, Delu Zeng, Yue Huang, John Paisley
Our objective incorporates the L1/2-norm in a way that can leverage recent computationally efficient methods, and L1 for which the alternating direction method of multipliers can be used.