Search Results for author: Ding Liu

Found 44 papers, 20 papers with code

Learning Progressive Joint Propagation for Human Motion Prediction

no code implementations ECCV 2020 Yujun Cai, Lin Huang, Yiwei Wang, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Xu Yang, Yiheng Zhu, Xiaohui Shen, Ding Liu, Jing Liu, Nadia Magnenat Thalmann

Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.

Human motion prediction motion prediction

Residual Local Feature Network for Efficient Super-Resolution

1 code implementation16 May 2022 Fangyuan Kong, Mingxi Li, Songwei Liu, Ding Liu, Jingwen He, Yang Bai, Fangmin Chen, Lean Fu

Moreover, we revisit the popular contrastive loss and observe that the selection of intermediate features of its feature extractor has great influence on the performance.

Image Super-Resolution Single Image Super Resolution +1

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

2 code implementations11 May 2022 Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang

The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.

Image Super-Resolution Single Image Super Resolution

Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution

1 code implementation18 Apr 2022 Zongcai Du, Ding Liu, Jie Liu, Jie Tang, Gangshan Wu, Lean Fu

Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution.

Image Super-Resolution

Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation

no code implementations19 May 2021 Giovanni Bonetta, Rossella Cancelliere, Ding Liu, Paul Vozila

Transformer-based models have demonstrated excellent capabilities of capturing patterns and structures in natural language generation and achieved state-of-the-art results in many tasks.

Response Generation Text Generation

Understanding Neural Networks with Logarithm Determinant Entropy Estimator

no code implementations8 May 2021 Zhanghao Zhouyin, Ding Liu

Understanding the informative behaviour of deep neural networks is challenged by misused estimators and the complexity of network structure, which leads to inconsistent observations and diversified interpretation.

Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis

2 code implementations12 Apr 2021 Xiaoyu Xiang, Ding Liu, Xiao Yang, Yiheng Zhu, Xiaohui Shen, Jan P. Allebach

In this paper, we explore open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are missing in the training data.

Domain Adaptation Image-to-Image Translation +1

Progressive Temporal Feature Alignment Network for Video Inpainting

1 code implementation CVPR 2021 Xueyan Zou, Linjie Yang, Ding Liu, Yong Jae Lee

To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content.

Optical Flow Estimation Video Inpainting

A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder

no code implementations ICCV 2021 Yujun Cai, Yiwei Wang, Yiheng Zhu, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Chuanxia Zheng, Sijie Yan, Henghui Ding, Xiaohui Shen, Ding Liu, Nadia Magnenat Thalmann

Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.

motion prediction motion synthesis

Neural Sparse Representation for Image Restoration

1 code implementation NeurIPS 2020 Yuchen Fan, Jiahui Yu, Yiqun Mei, Yulun Zhang, Yun Fu, Ding Liu, Thomas S. Huang

Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks.

Image Compression Image Denoising +2

Outlier Detection Using a Novel method: Quantum Clustering

no code implementations8 Jun 2020 Ding Liu, Hui Li

This approach, called Quantum Clustering (QC), deals with unlabeled data processing and constructs a potential function to find the centroids of clusters and the outliers.

Outlier Detection

Quantum-Classical Machine learning by Hybrid Tensor Networks

1 code implementation15 May 2020 Ding Liu, Zekun Yao, Quan Zhang

In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning.

Tensor Networks

Unsupervised Low-light Image Enhancement with Decoupled Networks

no code implementations6 May 2020 Wei Xiong, Ding Liu, Xiaohui Shen, Chen Fang, Jiebo Luo

In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion.

Image-to-Image Translation Low-Light Image Enhancement

Pyramid Attention Networks for Image Restoration

2 code implementations28 Apr 2020 Yiqun Mei, Yuchen Fan, Yulun Zhang, Jiahui Yu, Yuqian Zhou, Ding Liu, Yun Fu, Thomas S. Huang, Humphrey Shi

Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.

Demosaicking Image Denoising +1

Scale-wise Convolution for Image Restoration

1 code implementation19 Dec 2019 Yuchen Fan, Jiahui Yu, Ding Liu, Thomas S. Huang

In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance.

Data Augmentation Image Compression +3

DAVID: Dual-Attentional Video Deblurring

no code implementations7 Dec 2019 Junru Wu, Xiang Yu, Ding Liu, Manmohan Chandraker, Zhangyang Wang

To train and evaluate on more diverse blur severity levels, we propose a Challenging DVD dataset generated from the raw DVD video set by pooling frames with different temporal windows.

Deblurring

Information Bottleneck Theory on Convolutional Neural Networks

no code implementations9 Nov 2019 Junjie Li, Ding Liu

Recent years, many researches attempt to open the black box of deep neural networks and propose a various of theories to understand it.

EnlightenGAN: Deep Light Enhancement without Paired Supervision

8 code implementations17 Jun 2019 Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?

Image Restoration Low-Light Image Enhancement

Generative Tensor Network Classification Model for Supervised Machine Learning

no code implementations26 Mar 2019 Zheng-Zhi Sun, Cheng Peng, Ding Liu, Shi-Ju Ran, Gang Su

By investigating the distances in the many-body Hilbert space, we find that (a) the samples are naturally clustering in such a space; and (b) bounding the bond dimensions of the TN's to finite values corresponds to removing redundant information in the image recognition.

Classification General Classification

Connecting Image Denoising and High-Level Vision Tasks via Deep Learning

1 code implementation6 Sep 2018 Ding Liu, Bihan Wen, Jianbo Jiao, Xian-Ming Liu, Zhangyang Wang, Thomas S. Huang

Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation.

Computer Vision Image Denoising

Non-Local Recurrent Network for Image Restoration

1 code implementation NeurIPS 2018 Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, Thomas S. Huang

The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood.

Image Denoising Image Restoration +1

Survey of Face Detection on Low-quality Images

no code implementations19 Apr 2018 Yuqian Zhou, Ding Liu, Thomas Huang

However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems.

Face Detection Robust Design

Learning Simple Thresholded Features with Sparse Support Recovery

no code implementations16 Apr 2018 Hongyu Xu, Zhangyang Wang, Haichuan Yang, Ding Liu, Ji Liu

The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process.

Dictionary Learning

Enhance Visual Recognition under Adverse Conditions via Deep Networks

no code implementations20 Dec 2017 Ding Liu, Bowen Cheng, Zhangyang Wang, Haichao Zhang, Thomas S. Huang

Visual recognition under adverse conditions is a very important and challenging problem of high practical value, due to the ubiquitous existence of quality distortions during image acquisition, transmission, or storage.

Data Augmentation Image Restoration +3

Improving Object Detection from Scratch via Gated Feature Reuse

2 code implementations4 Dec 2017 Zhiqiang Shen, Honghui Shi, Jiahui Yu, Hai Phan, Rogerio Feris, Liangliang Cao, Ding Liu, Xinchao Wang, Thomas Huang, Marios Savvides

In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i. e., without pre-trained models).

object-detection Object Detection

Robust Video Super-Resolution With Learned Temporal Dynamics

no code implementations ICCV 2017 Ding Liu, Zhaowen Wang, Yuchen Fan, Xian-Ming Liu, Zhangyang Wang, Shiyu Chang, Thomas Huang

Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network that is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner.

Video Super-Resolution

Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach

no code implementations10 Sep 2017 Bowen Cheng, Zhangyang Wang, Zhaobin Zhang, Zhu Li, Ding Liu, Jianchao Yang, Shuai Huang, Thomas S. Huang

Emotion recognition from facial expressions is tremendously useful, especially when coupled with smart devices and wireless multimedia applications.

Emotion Recognition Super-Resolution

Learning audio sequence representations for acoustic event classification

no code implementations27 Jul 2017 Zixing Zhang, Ding Liu, Jing Han, Kun Qian, Björn Schuller

Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.

Classification General Classification

Understanding Convolution for Semantic Segmentation

5 code implementations27 Feb 2017 Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, Garrison Cottrell

This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.

Semantic Segmentation Thermal Image Segmentation

Learning a Mixture of Deep Networks for Single Image Super-Resolution

no code implementations3 Jan 2017 Ding Liu, Zhaowen Wang, Nasser Nasrabadi, Thomas Huang

This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem.

Image Super-Resolution Single Image Super Resolution

Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

1 code implementation journals 2016 Ding Liu, Zhaowen Wang, Bihan Wen, Student Member, Jianchao Yang, Member, Wei Han, and Thomas S. Huang, Fellow, IEEE

We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.

Image Super-Resolution Single Image Super Resolution

D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images

no code implementations CVPR 2016 Zhangyang Wang, Ding Liu, Shiyu Chang, Qing Ling, Yingzhen Yang, Thomas S. Huang

In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images.

$\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images

no code implementations16 Jan 2016 Zhangyang Wang, Ding Liu, Shiyu Chang, Qing Ling, Yingzhen Yang, Thomas S. Huang

In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images.

Deep Networks for Image Super-Resolution with Sparse Prior

no code implementations ICCV 2015 Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, Thomas Huang

We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end.

Computer Vision Image Restoration +1

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