no code implementations • 16 Jul 2023 • Wuyuan Xie, Miaohui Wang, Di Lin, Boxin Shi, Jianmin Jiang
With the rapid development of high-resolution 3D vision applications, the traditional way of manipulating surface detail requires considerable memory and computing time.
1 code implementation • 23 Apr 2023 • Yu Zhou, Yu Chen, Xiao Zhang, Pan Lai, Lei Huang, Jianmin Jiang
While the initial reconstruction sub-network has a hierarchical structure to progressively recover the image, reducing the number of parameters, the residual reconstruction sub-network facilitates recurrent residual feature extraction via recurrent learning to perform both feature fusion and deep reconstructions across different scales.
1 code implementation • CVPR 2022 • Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, Jianmin Jiang
Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement.
no code implementations • 11 Sep 2020 • Jinghua Wang, Jianmin Jiang
In this paper, we propose a deep spectral analysis network for unsupervised representation learning and image clustering.
no code implementations • 11 Sep 2020 • Jinghua Wang, Jianmin Jiang
In comparison with the existing work in similar areas, our objective function has two learning targets, which are created to be jointly optimized to achieve the best possible unsupervised learning and knowledge discovery from unlabeled data sets.
no code implementations • ECCV 2020 • Jinghua Wang, Jianmin Jiang
With the hypothesis that the shift between a given pair of domains is shared across tasks, we propose a new method for ZSDA by transferring domain shift from an irrelevant task (IrT) to the task of interest (ToI).
no code implementations • 11 Sep 2020 • Jinghua Wang, Adrian Hilton, Jianmin Jiang
This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis, which is a popular technique with solid theory foundations.
no code implementations • ICCV 2019 • Jinghua Wang, Jianmin Jiang
To train CoCoGAN in the absence of target-domain data for RT, we propose a new supervisory signal, i. e. the alignment between representations across tasks.
5 code implementations • CVPR 2019 • Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Jiashi Feng, Jianmin Jiang
We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway.
Ranked #1 on RGB Salient Object Detection on SOD
no code implementations • 12 Apr 2019 • Bin Sun, Chen Chen, Yingying Zhu, Jianmin Jiang
The task of cross-view image geo-localization aims to determine the geo-location (GPS coordinates) of a query ground-view image by matching it with the GPS-tagged aerial (satellite) images in a reference dataset.
no code implementations • 21 Sep 2018 • Meijun Sun, Ziqi Zhou, QinGhua Hu, Zheng Wang, Jianmin Jiang
To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance.