no code implementations • ECCV 2020 • Tong He, Yifan Liu, Chunhua Shen, Xinlong Wang, Changming Sun
However, these methods are unaware of the instance context and fail to realize the boundary and geometric information of an instance, which are critical to separate adjacent objects.
no code implementations • ECCV 2020 • Saimunur Rahman, Lei Wang, Changming Sun, Luping Zhou
When learning this representation in deep networks, eigen-decomposition of covariance matrix is usually needed for a key step called matrix normalisation.
no code implementations • 13 Feb 2023 • Linwei Tao, Minjing Dong, Daochang Liu, Changming Sun, Chang Xu
However, early stopping, as a well-known technique to mitigate overfitting, fails to calibrate networks.
1 code implementation • 11 Oct 2022 • Tianling Liu, Ran Su, Changming Sun, Xiuting Li, Leyi Wei
Next, we developed a survival prediction model, named DeepConvAttentionSurv (DCAS), which was able to extract patch-level features, removed less discriminative clusters and predicted the EOC survival precisely.
no code implementations • 4 Apr 2022 • Libo Sun, Wei Yin, Enze Xie, Zhengrong Li, Changming Sun, Chunhua Shen
The core of our framework is a monocular depth estimation module with a strong generalization capability for diverse scenes.
no code implementations • 15 Jun 2021 • Junfeng Jing, Tian Gao, Weichuan Zhang, Yongsheng Gao, Changming Sun
The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed.
no code implementations • 13 Jun 2021 • Weichuan Zhang, Xuefang Liu, Zhe Xue, Yongsheng Gao, Changming Sun
Metric-based few-shot fine-grained image classification (FSFGIC) aims to learn a transferable feature embedding network by estimating the similarities between query images and support classes from very few examples.
1 code implementation • 20 Apr 2021 • Qiangguo Jin, Hui Cui, Changming Sun, Zhaopeng Meng, Leyi Wei, Ran Su
DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine segmentation results.
1 code implementation • 20 Apr 2021 • Qiangguo Jin, Hui Cui, Changming Sun, Zhaopeng Meng, Ran Su
The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function.
no code implementations • 10 May 2020 • Miaohua Zhang, Yongsheng Gao, Changming Sun, Michael Blumenstein
Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the observation data.
no code implementations • 10 May 2020 • Miaohua Zhang, Yongsheng Gao, Changming Sun, Michael Blumenstein
Traditional tensor decomposition methods, e. g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers.
2 code implementations • 3 Feb 2020 • Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, Dou Renyin
Compared with previous learning objectives, i. e., learning metric depth or relative depth, we propose to learn the affine-invariant depth using our diverse dataset to ensure both generalization and high-quality geometric shapes of scenes.
no code implementations • 20 Nov 2019 • Saimunur Rahman, Lei Wang, Changming Sun, Luping Zhou
This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods.
1 code implementation • CVPR 2019 • Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan
To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride.
1 code implementation • 4 Nov 2018 • Qiangguo Jin, Zhaopeng Meng, Changming Sun, Leyi Wei, Ran Su
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes.
2 code implementations • CVPR 2018 • Tong He, Zhi Tian, Weilin Huang, Chunhua Shen, Yu Qiao, Changming Sun
This allows the two tasks to work collaboratively by shar- ing convolutional features, which is critical to identify challenging text instances.
no code implementations • CVPR 2017 • Bin Wang, Yongsheng Gao, Changming Sun, Michael Blumenstein, John La Salle
A novel chord bunch walks (CBW) descriptor is developed through the chord walking that effectively integrates the shape image function over the walked chord to reflect the contour features and the inner properties of the shape.
no code implementations • CVPR 2014 • Xiao Tan, Changming Sun, Tuan D. Pham
By using the hybrid of the local polynomial model and color/intensity based range guidance, the proposed method not only preserves edges but also does a much better job in preserving spatial variation than existing popular filtering methods.