315 citations · 2.4k across the 76 of their papers we have counts for
12 papers · 1 filter
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
Yapeng Tian, Yulun Zhang, Yun Fu +1
Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple…
Residual Dense Network for Image Restoration
Yulun Zhang, Yapeng Tian, Yu Kong +2
Convolutional neural network has recently achieved great success for image restoration (IR) and also offered hierarchical features. However, most deep CNN based IR models do not ma…
Visual Font Pairing
Shuhui Jiang, Zhaowen Wang, Aaron Hertzmann +2
This paper introduces the problem of automatic font pairing. Font pairing is an important design task that is difficult for novices. Given a font selection for one part of a docume…
Arc-support Line Segments Revisited: An Efficient and High-quality Ellipse Detection
Changsheng Lu, Siyu Xia, Ming Shao +1
Over the years many ellipse detection algorithms spring up and are studied broadly, while the critical issue of detecting ellipses accurately and efficiently in real-world images r…
Support Neighbor Loss for Person Re-Identification
Kai Li, Zhengming Ding, Kunpeng Li +2
Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks (CNN). The majority of deep re-ID methods focus…
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Yulun Zhang, Kunpeng Li, Kai Li +3
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train…
Human Action Recognition and Prediction: A Survey
Yu Kong, Yun Fu
Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving from inferring the present state to predicting the future state. Vision-b…
Predictive Local Smoothness for Stochastic Gradient Methods
Jun Li, Hongfu Liu, Bineng Zhong +2
Stochastic gradient methods are dominant in nonconvex optimization especially for deep models but have low asymptotical convergence due to the fixed smoothness. To address this pro…
Residual Dense Network for Image Super-Resolution
Yulun Zhang, Yapeng Tian, Yu Kong +2
A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN…
Tell Me Where to Look: Guided Attention Inference Network
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng +2
Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps…
Incremental Classifier Learning with Generative Adversarial Networks
Yue Wu, Yinpeng Chen, Lijuan Wang +5
In this paper, we address the incremental classifier learning problem, which suffers from catastrophic forgetting. The main reason for catastrophic forgetting is that the past data…
Clustering with Outlier Removal
Hongfu Liu, Jun Li, Yue Wu +1
Cluster analysis and outlier detection are strongly coupled tasks in data mining area. Cluster structure can be easily destroyed by few outliers; on the contrary, outliers are defi…