1 code implementation • 9 Jun 2023 • Jie Gui, Xiaofeng Cong, Lei He, Yuan Yan Tang, James Tin-Yau Kwok
On the one hand, the dehazing task is an illposedness problem, which means that no unique solution exists.
no code implementations • 28 Apr 2023 • Shengchao Chen, Ting Shu, Huan Zhao, Yuan Yan Tang
The proposed model called MASK-Convolutional Neural Network-Transformer (MASK-CT) is based on the Transformer, the convolutional process, and the MASK mechanism.
1 code implementation • 30 Mar 2023 • Jie Gui, Xiaofeng Cong, Chengwei Peng, Yuan Yan Tang, James Tin-Yau Kwok
In this paper, we focus on designing a group of attack methods based on first order gradient to verify the robustness of the existing dehazing algorithms.
no code implementations • 16 Nov 2022 • Biwei Cao, Jiuxin Cao, Jie Gui, Jiayun Shen, Bo Liu, Lei He, Yuan Yan Tang, James Tin-Yau Kwok
Such approaches, however, ignore the VE's unique nature of relation inference between the premise and hypothesis.
1 code implementation • 28 Sep 2021 • Xiaoliu Luo, Zhuotao Tian, Taiping Zhang, Bei Yu, Yuan Yan Tang, Jiaya Jia
In this work, we revisit the prior mask guidance proposed in ``Prior Guided Feature Enrichment Network for Few-Shot Segmentation''.
no code implementations • 17 Sep 2020 • Anyong Qin, Lina Xian, Yong-Liang Yang, Taiping Zhang, Yuan Yan Tang
The recovery of the underlying low-rank structure of clean data corrupted with sparse noise/outliers is attracting increasing interest.
no code implementations • 17 Sep 2020 • Anyong Qin, Zhaowei Shang, Zhuolin Tan, Taiping Zhang, Yuan Yan Tang
Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly.
no code implementations • 8 Apr 2019 • Lefei Zhang, Qian Zhang, Bo Du, Xin Huang, Yuan Yan Tang, DaCheng Tao
In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.
no code implementations • 29 Jan 2019 • Yang Lu, Yiu-ming Cheung, Yuan Yan Tang
To the best of our knowledge, there is no any measurement about the extent of influence of class imbalance on the classification performance of imbalanced data.
no code implementations • 24 Nov 2017 • Danping Liao, Yuntao Qian, Yuan Yan Tang
A composite kernel is applied in manifold learning to incorporate both the spatial and spectral information of HSI in the embedded space.
no code implementations • 5 Nov 2017 • Yulong Wang, Yuan Yan Tang, Luoqing Li, Hong Chen
In this paper, we propose a modal regression based atomic representation and classification (MRARC) framework to alleviate such limitation.
no code implementations • 5 Sep 2015 • Yuewei Lin, Jing Chen, Yu Cao, Youjie Zhou, Lingfeng Zhang, Yuan Yan Tang, Song Wang
By adopting a natural and widely used assumption -- "the data samples from the same class should lay on a low-dimensional subspace, even if they come from different domains", the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the compact joint subspaces of source and target domain.