483 citations · 2.7k across the 59 of their papers we have counts for
13 papers · 1 filter
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning
Chen Wei, Lingxi Xie, Xutong Ren +5
Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information. We conside…
Temporal Bilinear Networks for Video Action Recognition
Yanghao Li, Sijie Song, Yuqi Li +1
Temporal modeling in videos is a fundamental yet challenging problem in computer vision. In this paper, we propose a novel Temporal Bilinear (TB) model to capture the temporal pair…
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning
Rui Qian, Yunchao Wei, Honghui Shi +3
Semantic scene parsing is suffering from the fact that pixel-level annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PD…
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Xutong Ren, Lingxi Xie, Chen Wei +6
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn. Coarse-to-fine (C2F)…
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
Xiang Gao, Wei Hu, Jiaxiang Tang +2
With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition. Previous methods model skel…
Context-Aware Text-Based Binary Image Stylization and Synthesis
Shuai Yang, Jiaying Liu, Wenhan Yang +1
In this work, we present a new framework for the stylization of text-based binary images. First, our method stylizes the stroke-based geometric shape like text, symbols and icons i…
Retrieval-Enhanced Adversarial Training for Neural Response Generation
Qingfu Zhu, Lei Cui, Weinan Zhang +2
Dialogue systems are usually built on either generation-based or retrieval-based approaches, yet they do not benefit from the advantages of different models. In this paper, we prop…
Deep Retinex Decomposition for Low-Light Enhancement
Chen Wei, Wenjing Wang, Wenhan Yang +1
Retinex model is an effective tool for low-light image enhancement. It assumes that observed images can be decomposed into the reflectance and illumination. Most existing Retinex-b…
Progressive Spatial Recurrent Neural Network for Intra Prediction
Yueyu Hu, Wenhan Yang, Mading Li +1
Intra prediction is an important component of modern video codecs, which is able to efficiently squeeze out the spatial redundancy in video frames. With preceding pixels as the con…
A Group Variational Transformation Neural Network for Fractional Interpolation of Video Coding
Sifeng Xia, Wenhan Yang, Yueyu Hu +2
Motion compensation is an important technology in video coding to remove the temporal redundancy between coded video frames. In motion compensation, fractional interpolation is use…
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal
Xiaoshuai Zhang, Wenhan Yang, Yueyu Hu +1
JPEG is one of the most commonly used standards among lossy image compression methods. However, JPEG compression inevitably introduces various kinds of artifacts, especially at hig…
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration
Xiaoshuai Zhang, Yiping Lu, Jiaying Liu +1
In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model. The proposed control…
Joint Enhancement and Denoising Method via Sequential Decomposition
Xutong Ren, Mading Li, Wen-Huang Cheng +1
Many low-light enhancement methods ignore intensive noise in original images. As a result, they often simultaneously enhance the noise as well. Furthermore, extra denoising procedu…