309 citations · 1.6k across the 77 of their papers we have counts for
17 papers · 1 filter
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
Feng Li, Runmin Cong, Huihui Bai +3
Recently, convolutional neural network (CNN) has demonstrated significant success for image restoration (IR) tasks (e.g., image super-resolution, image deblurring, rain streak remo…
Decision-based Universal Adversarial Attack
Jing Wu, Mingyi Zhou, Shuaicheng Liu +2
A single perturbation can pose the most natural images to be misclassified by classifiers. In black-box setting, current universal adversarial attack methods utilize substitute mod…
Siamese Network for RGB-D Salient Object Detection and Beyond
Keren Fu, Deng-Ping Fan, Ge-Peng Ji +3
Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such sche…
Deep Embedded Multi-view Clustering with Collaborative Training
Jie Xu, Yazhou Ren, Guofeng Li +3
Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with hig…
Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector Tensor Factorization
Zhen Long, Yipeng Liu, Sixing Zeng +3
Hyperspectral image (HSI) has some advantages over natural image for various applications due to the extra spectral information. During the acquisition, it is often contaminated by…
Bayesian Low Rank Tensor Ring Model for Image Completion
Zhen Long, Ce Zhu, Jiani Liu +1
Low rank tensor ring model is powerful for image completion which recovers missing entries in data acquisition and transformation. The recently proposed tensor ring (TR) based comp…
Unsupervised Feature Selection via Multi-step Markov Transition Probability
Yan Min, Mao Ye, Liang Tian +3
Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good result…
Disentanglement Then Reconstruction: Learning Compact Features for Unsupervised Domain Adaptation
Lihua Zhou, Mao Ye, Xinpeng Li +3
Recent works in domain adaptation always learn domain invariant features to mitigate the gap between the source and target domains by adversarial methods. The category information…
Learning Various Length Dependence by Dual Recurrent Neural Networks
Chenpeng Zhang, Shuai Li, Mao Ye +2
Recurrent neural networks (RNNs) are widely used as a memory model for sequence-related problems. Many variants of RNN have been proposed to solve the gradient problems of training…
ProbaNet: Proposal-balanced Network for Object Detection
Jing Wu, Xiang Zhang, Mingyi Zhou +1
Candidate object proposals generated by object detectors based on convolutional neural network (CNN) encounter easy-hard samples imbalance problem, which can affect overall perform…
The Power of Triply Complementary Priors for Image Compressive Sensing
Zhiyuan Zha, Xin Yuan, Joey Tianyi Zhou +3
Recent works that utilized deep models have achieved superior results in various image restoration applications. Such approach is typically supervised which requires a corpus of tr…
Hierarchical Tensor Ring Completion
Abdul Ahad, Zhen Long, Ce Zhu +1
Tensor completion can estimate missing values of a high-order data from its partially observed entries. Recent works show that low rank tensor ring approximation is one of the most…
AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
Zhonghao Zhang, Yipeng Liu, Jiani Liu +2
Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative opt…
Frequency-Weighted Robust Tensor Principal Component Analysis
Shenghan Wang, Yipeng Liu, Lanlan Feng +1
Robust tensor principal component analysis (RTPCA) can separate the low-rank component and sparse component from multidimensional data, which has been used successfully in several…
Adversarial Imitation Attack
Mingyi Zhou, Jing Wu, Yipeng Liu +4
Deep learning models are known to be vulnerable to adversarial examples. A practical adversarial attack should require as little as possible knowledge of attacked models. Current s…
DaST: Data-free Substitute Training for Adversarial Attacks
Mingyi Zhou, Jing Wu, Yipeng Liu +2
Machine learning models are vulnerable to adversarial examples. For the black-box setting, current substitute attacks need pre-trained models to generate adversarial examples. Howe…
A Unified Framework for Coupled Tensor Completion
Huyan Huang, Yipeng Liu, Ce Zhu
Coupled tensor decomposition reveals the joint data structure by incorporating priori knowledge that come from the latent coupled factors. The tensor ring (TR) decomposition is inv…