341 citations · 1.9k across the 24 of their papers we have counts for
25 papers
End-to-End Image Compression with Probabilistic Decoding
Haichuan Ma, Dong Liu, Cunhui Dong +2
Lossy image compression is a many-to-one process, thus one bitstream corresponds to multiple possible original images, especially at low bit rates. However, this nature was seldom…
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
Yujing Hu, Weixun Wang, Hangtian Jia +5
Reward shaping is an effective technique for incorporating domain knowledge into reinforcement learning (RL). Existing approaches such as potential-based reward shaping normally ma…
M-LVC: Multiple Frames Prediction for Learned Video Compression
Jianping Lin, Dong Liu, Houqiang Li +1
We propose an end-to-end learned video compression scheme for low-latency scenarios. Previous methods are limited in using the previous one frame as reference. Our method introduce…
Camera Trace Erasing
Chang Chen, Zhiwei Xiong, Xiaoming Liu +1
Camera trace is a unique noise produced in digital imaging process. Most existing forensic methods analyze camera trace to identify image origins. In this paper, we address a new l…
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement
Yang Wang, Yang Cao, Zheng-Jun Zha +4
Contrast enhancement and noise removal are coupled problems for low-light image enhancement. The existing Retinex based methods do not take the coupling relation into consideration…
LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network
Yuhang Li, Xuejin Chen, Feng Wu +1
In this paper, we explore the task of generating photo-realistic face images from lines. Previous methods based on conditional generative adversarial networks (cGANs) have shown th…
Generalized Fault-Tolerance Topology Generation for Application Specific Network-on-Chips
Song Chen, Mengke Ge, Zhigang Li +3
The Network-on-Chips is a promising candidate for addressing communication bottlenecks in many-core processors and neural network processors. In this work, we consider the generali…
Context-Aware Visual Policy Network for Fine-Grained Image Captioning
Zheng-Jun Zha, Daqing Liu, Hanwang Zhang +2
With the maturity of visual detection techniques, we are more ambitious in describing visual content with open-vocabulary, fine-grained and free-form language, i.e., the task of im…
Deep Learning-Based Video Coding: A Review and A Case Study
Dong Liu, Yue Li, Jianping Lin +2
The past decade has witnessed great success of deep learning technology in many disciplines, especially in computer vision and image processing. However, deep learning-based video…
Camera Lens Super-Resolution
Chang Chen, Zhiwei Xiong, Xinmei Tian +2
Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling. In this paper, we invest…
Learning Deterministic Policy with Target for Power Control in Wireless Networks
Yujiao Lu, Hancheng Lu, Liangliang Cao +2
Inter-Cell Interference Coordination (ICIC) is a promising way to improve energy efficiency in wireless networks, especially where small base stations are densely deployed. However…
Sequential Gating Ensemble Network for Noise Robust Multi-Scale Face Restoration
Zhibo Chen, Jianxin Lin, Tiankuang Zhou +1
Face restoration from low resolution and noise is important for applications of face analysis recognition. However, most existing face restoration models omit the multiple scale is…
Learning to Assemble Neural Module Tree Networks for Visual Grounding
Daqing Liu, Hanwang Zhang, Feng Wu +1
Visual grounding, a task to ground (i.e., localize) natural language in images, essentially requires composite visual reasoning. However, existing methods over-simplify the composi…
Context-Aware Visual Policy Network for Sequence-Level Image Captioning
Daqing Liu, Zheng-Jun Zha, Hanwang Zhang +2
Many vision-language tasks can be reduced to the problem of sequence prediction for natural language output. In particular, recent advances in image captioning use deep reinforceme…
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels
Shaobo Min, Xuejin Chen, Zheng-Jun Zha +2
\begin{abstract} Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation. Although many semi-supervised methods have been propos…
Towards Optimal Power Control via Ensembling Deep Neural Networks
Fei Liang, Cong Shen, Wei Yu +1
A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interferenc…
Learning for Video Compression
Zhibo Chen, Tianyu He, Xin Jin +1
One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In…
Neural network-based arithmetic coding of intra prediction modes in HEVC
Rui Song, Dong Liu, Houqiang Li +1
In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well…
An Iterative BP-CNN Architecture for Channel Decoding
Fei Liang, Cong Shen, Feng Wu
Inspired by recent advances in deep learning, we propose a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained…
A Convolutional Neural Network Approach for Half-Pel Interpolation in Video Coding
Ning Yan, Dong Liu, Houqiang Li +1
Motion compensation is a fundamental technology in video coding to remove the temporal redundancy between video frames. To further improve the coding efficiency, sub-pel motion com…
An Efficient Four-Parameter Affine Motion Model for Video Coding
Li Li, Houqiang Li, Dong Liu +4
In this paper, we study a simplified affine motion model based coding framework to overcome the limitation of translational motion model and maintain low computational complexity.…
Convolutional Neural Network-Based Block Up-sampling for Intra Frame Coding
Yue Li, Dong Liu, Houqiang Li +4
Inspired by the recent advances of image super-resolution using convolutional neural network (CNN), we propose a CNN-based block up-sampling scheme for intra frame coding. A block…
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
Yuanying Dai, Dong Liu, Feng Wu
Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit-rates. To reduce these artifacts, po…
Deeply Exploit Depth Information for Object Detection
Saihui Hou, Zilei Wang, Feng Wu
This paper addresses the issue on how to more effectively coordinate the depth with RGB aiming at boosting the performance of RGB-D object detection. Particularly, we investigate t…