9.2k citations · 16.2k across the 175 of their papers we have counts for
22 papers · 1 filter
Deep Learning on Image Denoising: An overview
Chunwei Tian, Lunke Fei, Wenxian Zheng +3
Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods deali…
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Qilong Wang, Banggu Wu, Pengfei Zhu +3
Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing me…
Perspective-Guided Convolution Networks for Crowd Counting
Zhaoyi Yan, Yuchen Yuan, Wangmeng Zuo +4
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i.e. PGCNet), which aims to overcome the dramati…
Image Inpainting with Learnable Bidirectional Attention Maps
Chaohao Xie, Shaohui Liu, Chao Li +5
Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular ho…
Deep Concept-wise Temporal Convolutional Networks for Action Localization
Xin Li, Tianwei Lin, Xiao Liu +7
Existing action localization approaches adopt shallow temporal convolutional networks (\ie, TCN) on 1D feature map extracted from video frames. In this paper, we empirically find t…
Neural Blind Deconvolution Using Deep Priors
Dongwei Ren, Kai Zhang, Qilong Wang +2
Blind deconvolution is a classical yet challenging low-level vision problem with many real-world applications. Traditional maximum a posterior (MAP) based methods rely heavily on f…
Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
Hao Wang, Qilong Wang, Fan Yang +2
Collection of massive well-annotated samples is effective in improving object detection performance but is extremely laborious and costly. Instead of data collection and annotation…
Multi-level Wavelet Convolutional Neural Networks
Pengju Liu, Hongzhi Zhang, Wei Lian +1
In computer vision, convolutional networks (CNNs) often adopts pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can caus…
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
Mu Li, Kede Ma, Jane You +2
Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized imag…
Blind Super-Resolution With Iterative Kernel Correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo +1
Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume th…
Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How
Feng Li, Xiaohe Wu, Wangmeng Zuo +2
Correlation filters (CFs) have been continuously advancing the state-of-the-art tracking performance and have been extensively studied in the recent few years. Most of the existing…
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Ming Liu, Yukang Ding, Min Xia +4
Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoder-decoder usually…
DAVANet: Stereo Deblurring with View Aggregation
Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo +3
Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles. However, they also suffer from blurry images in dy…
Spatio-Temporal Filter Adaptive Network for Video Deblurring
Shangchen Zhou, Jiawei Zhang, Jinshan Pan +3
Video deblurring is a challenging task due to the spatially variant blur caused by camera shake, object motions, and depth variations, etc. Existing methods usually estimate optica…
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels
Yiwen Guo, Ming Lu, Wangmeng Zuo +2
Convolutional neural networks have been proven effective in a variety of image restoration tasks. Most state-of-the-art solutions, however, are trained using images with a single p…
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
Qilong Wang, Jiangtao Xie, Wangmeng Zuo +2
Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potenti…
Learning Content-Weighted Deep Image Compression
Mu Li, Wangmeng Zuo, Shuhang Gu +2
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance. Most existing methods adopt spatially invariant bit length allocation…
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
Kai Zhang, Wangmeng Zuo, Lei Zhang
While deep neural networks (DNN) based single image super-resolution (SISR) methods are rapidly gaining popularity, they are mainly designed for the widely-used bicubic degradation…
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation
Lei Zhang, Shanshan Wang, Guang-Bin Huang +3
In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e. non-i.i.d.). Maximum mean discrepancy (MMD), as a do…
Extreme Channel Prior Embedded Network for Dynamic Scene Deblurring
Jianrui Cai, Wangmeng Zuo, Lei Zhang
Recent years have witnessed the significant progress on convolutional neural networks (CNNs) in dynamic scene deblurring. While CNN models are generally learned by the reconstructi…
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Qi Xie, Minghao Zhou, Qian Zhao +3
Hyperspectral imaging can help better understand the characteristics of different materials, compared with traditional image systems. However, only high-resolution multispectral (H…
Progressive Image Deraining Networks: A Better and Simpler Baseline
Dongwei Ren, Wangmeng Zuo, Qinghua Hu +2
Along with the deraining performance improvement of deep networks, their structures and learning become more and more complicated and diverse, making it difficult to analyze the co…