114 citations · 365 across the 15 of their papers we have counts for
15 papers
URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering
Ge Teng, Ting Mao, Chen Shen +4
Incomplete multi-view clustering (IMVC) aims to cluster multi-view data that are only partially available. This poses two main challenges: effectively leveraging multi-view informa…
Self-Learning Symmetric Multi-view Probabilistic Clustering
Junjie Liu, Junlong Liu, Rongxin Jiang +3
Multi-view Clustering (MVC) has achieved significant progress, with many efforts dedicated to learn knowledge from multiple views. However, most existing methods are either not app…
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
Yao Zhu, Yuefeng Chen, Xiaodan Li +4
With the development of deep learning technology, the facial manipulation system has become powerful and easy to use. Such systems can modify the attributes of the given facial ima…
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective
Yao Zhu, Yuefeng Chen, Xiaodan Li +6
Out-Of-Distribution (OOD) detection has received broad attention over the years, aiming to ensure the reliability and safety of deep neural networks (DNNs) in real-world scenarios…
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
Yao Zhu, Yuefeng Chen, Xiaodan Li +6
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and t…
Boosting Out-of-distribution Detection with Typical Features
Yao Zhu, YueFeng Chen, Chuanlong Xie +6
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD det…
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection
Ze Chen, Zhihang Fu, Jianqiang Huang +5
Weakly supervised object detection (WSOD), which is an effective way to train an object detection model using only image-level annotations, has attracted considerable attention fro…
Dynamic Supervisor for Cross-dataset Object Detection
Ze Chen, Zhihang Fu, Jianqiang Huang +6
The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learn…
DGL-GAN: Discriminator Guided Learning for GAN Compression
Yuesong Tian, Li Shen, Xiang Tian +4
Generative Adversarial Networks (GANs) with high computation costs, e.g., BigGAN and StyleGAN2, have achieved remarkable results in synthesizing high-resolution images from random…
PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation
Shaotian Yan, Chen Shen, Zhongming Jin +4
Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution. Thus, tackling…
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection
Ze Chen, Zhihang Fu, Rongxin Jiang +2
Based on the framework of multiple instance learning (MIL), tremendous works have promoted the advances of weakly supervised object detection (WSOD). However, most MIL-based method…
AIM 2019 Challenge on Image Demoireing: Methods and Results
Shanxin Yuan, Radu Timofte, Gregory Slabaugh +25
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper desc…
S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction
Bolun Zheng, Rui Sun, Xiang Tian +1
Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach e…
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
Bolun Zheng, Yaowu Chen, Xiang Tian +2
Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they suffer from handling color images…
Sharp Attention Network via Adaptive Sampling for Person Re-identification
Chen Shen, Guo-Jun Qi, Rongxin Jiang +4
In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem.…