Search Results for author: Dong Liu

Found 103 papers, 44 papers with code

Photon-Efficient 3D Imaging with A Non-Local Neural Network

1 code implementation ECCV 2020 Jiayong Peng, Zhiwei Xiong, Xin Huang, Zheng-Ping Li, Dong Liu, Feihu Xu

Photon-efficient imaging has enabled a number of applications relying on single-photon sensors that can capture a 3D image with as few as one photon per pixel.

Learning Trailer Moments in Full-Length Movies with Co-Contrastive Attention

no code implementations ECCV 2020 Lezi Wang, Dong Liu, Rohit Puri, Dimitris N. Metaxas

We introduce a novel ranking network that utilizes the Co-Attention between movies and trailers as guidance to generate the training pairs, where the moments highly corrected with trailers are expected to be scored higher than the uncorrelated moments.

On Uniform Scalar Quantization for Learned Image Compression

no code implementations29 Sep 2023 Haotian Zhang, Li Li, Dong Liu

In principle, we find two factors crucial: one is the discrepancy between the surrogate and rounding, leading to train-test mismatch; the other is gradient estimation risk due to the surrogate, which consists of bias and variance of the gradient estimation.

Learning Fine-Grained Features for Pixel-wise Video Correspondences

1 code implementation ICCV 2023 Rui Li, Shenglong Zhou, Dong Liu

We address the problem of learning features for establishing pixel-wise correspondences.

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

no code implementations4 Aug 2023 Haowen Wang, Zhipeng Fan, Zhen Zhao, Zhengping Che, Zhiyuan Xu, Dong Liu, Feifei Feng, Yakun Huang, XIUQUAN QIAO, Jian Tang

We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene.

Pose Estimation

On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement

1 code implementation ICCV 2023 Xin Luo, Yunan Zhu, Shunxin Xu, Dong Liu

We tackle this issue by examining the spectral discriminators in the context of perceptual image super-resolution (i. e., GAN-based SR), as SR image quality is susceptible to spectral changes.

Image Super-Resolution No-Reference Image Quality Assessment

Offline and Online Optical Flow Enhancement for Deep Video Compression

no code implementations11 Jul 2023 Chuanbo Tang, Xihua Sheng, Zhuoyuan Li, Haotian Zhang, Li Li, Dong Liu

In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e. g. H. 266/VVC, as we believe the motion information of H. 266/VVC achieves a better rate-distortion trade-off.

Motion Estimation Optical Flow Estimation +1

LVVC: A Learned Versatile Video Coding Framework for Efficient Human-Machine Vision

no code implementations19 Jun 2023 Xihua Sheng, Li Li, Dong Liu, Houqiang Li

Such compact representations need to be decoded back to pixels before being displayed to human and - as usual - before being processed/analyzed by machine vision algorithms.

Video Reconstruction

A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty

1 code implementation7 Jun 2023 Kaidong Zhang, Ziyang Gan, Dong Liu, Xifu Shang

For THA, it is of clinical significance to analyze the bone structure from the CT images, especially to observe the structure of the acetabulum and femoral head, before the surgical procedure.

Active Learning Anatomy +3

Towards Interactive Image Inpainting via Sketch Refinement

1 code implementation1 Jun 2023 Chang Liu, Shunxin Xu, Jialun Peng, Kaidong Zhang, Dong Liu

To address this problem, we propose a two-stage image inpainting method termed SketchRefiner.

Image Inpainting

Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment

no code implementations22 May 2023 Hongbin Lin, Mingkui Tan, Yifan Zhang, Zhen Qiu, Shuaicheng Niu, Dong Liu, Qing Du, Yanxia Liu

To address this issue, we study a more practical SF-UDA task, termed imbalance-agnostic SF-UDA, where the class distributions of both the unseen source domain and unlabeled target domain are unknown and could be arbitrarily skewed.

Pseudo Label Source-Free Domain Adaptation +1

Late-Constraint Diffusion Guidance for Controllable Image Synthesis

1 code implementation19 May 2023 Chang Liu, Dong Liu

Specifically, we train a lightweight condition adapter to establish the correlation between external conditions and internal representations of diffusion models.

Conditional Image Generation Conditional Text-to-Image Synthesis

Customized Segment Anything Model for Medical Image Segmentation

1 code implementation26 Apr 2023 Kaidong Zhang, Dong Liu

Different from the previous methods, SAMed is built upon the large-scale image segmentation model, Segment Anything Model (SAM), to explore the new research paradigm of customizing large-scale models for medical image segmentation.

Image Segmentation Medical Image Segmentation +2

MRVM-NeRF: Mask-Based Pretraining for Neural Radiance Fields

no code implementations11 Apr 2023 Ganlin Yang, Guoqiang Wei, Zhizheng Zhang, Yan Lu, Dong Liu

Most Neural Radiance Fields (NeRFs) have poor generalization ability, limiting their application when representing multiple scenes by a single model.

Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting

2 code implementations24 Jan 2023 Kaidong Zhang, Jialun Peng, Jingjing Fu, Dong Liu

Transformers have been widely used for video processing owing to the multi-head self attention (MHSA) mechanism.

 Ranked #1 on Video Inpainting on DAVIS (SSIM (square) metric)

Optical Flow Estimation Video Inpainting

Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning

no code implementations ICCV 2023 Tiankang Su, Huihui Song, Dong Liu, Bo Liu, Qingshan Liu

We integrate our offline training and online fine-tuning in a unified framework for unsupervised video object segmentation and dub our method Online Adversarial Self-Tuning (OAST).

Pseudo Label Semantic Segmentation +2

Spatial-then-Temporal Self-Supervised Learning for Video Correspondence

1 code implementation CVPR 2023 Rui Li, Dong Liu

Specifically, we firstly extract spatial features from unlabeled images via contrastive learning, and secondly enhance the features by exploiting the temporal cues in unlabeled videos via reconstructive learning.

Contrastive Learning Self-Supervised Learning

Flow-Guided Transformer for Video Inpainting

1 code implementation14 Aug 2022 Kaidong Zhang, Jingjing Fu, Dong Liu

Especially in spatial transformer, we design a dual perspective spatial MHSA, which integrates the global tokens to the window-based attention.

Retrieval Video Inpainting

Towards Hybrid-Optimization Video Coding

no code implementations12 Jul 2022 Shuai Huo, Dong Liu, Li Li, Siwei Ma, Feng Wu, Wen Gao

Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently.

Neural Compression-Based Feature Learning for Video Restoration

no code implementations CVPR 2022 Cong Huang, Jiahao Li, Bin Li, Dong Liu, Yan Lu

The temporal features usually contain various noisy and uncorrelated information, and they may interfere with the restoration of the current frame.

Denoising Quantization +3

aiWave: Volumetric Image Compression with 3-D Trained Affine Wavelet-like Transform

no code implementations11 Mar 2022 Dongmei Xue, Haichuan Ma, Li Li, Dong Liu, Zhiwei Xiong

Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice.

Image Compression

Retinal Vessel Segmentation with Pixel-wise Adaptive Filters

1 code implementation3 Feb 2022 Mingxing Li, Shenglong Zhou, Chang Chen, Yueyi Zhang, Dong Liu, Zhiwei Xiong

Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast.

Retinal Vessel Segmentation

Motion-Focused Contrastive Learning of Video Representations

1 code implementation ICCV 2021 Rui Li, Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, Tao Mei

To this end, we compose a duet of exploiting the motion for data augmentation and feature learning in the regime of contrastive learning.

Contrastive Learning Data Augmentation +2

Inertia-Guided Flow Completion and Style Fusion for Video Inpainting

1 code implementation CVPR 2022 Kaidong Zhang, Jingjing Fu, Dong Liu

We propose a flow completion network to align and aggregate flow features from the consecutive flow sequences based on the inertia prior.

Optical Flow Estimation Video Inpainting

Attribute Artifacts Removal for Geometry-based Point Cloud Compression

no code implementations1 Dec 2021 Xihua Sheng, Li Li, Dong Liu, Zhiwei Xiong

In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC.

Graph Attention Quantization +1

Temporal Context Mining for Learned Video Compression

1 code implementation27 Nov 2021 Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, Yan Lu

From the stored propagated features, we propose to learn multi-scale temporal contexts, and re-fill the learned temporal contexts into the modules of our compression scheme, including the contextual encoder-decoder, the frame generator, and the temporal context encoder.


Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data

no code implementations28 Oct 2021 Dong Liu, Jiankang Zhang, Jingjing Cui, Soon-Xin Ng, Robert G. Maunder, Lajos Hanzo

Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks.

Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes

no code implementations28 Oct 2021 Dong Liu, Jingjing Cui, Jiankang Zhang, Chenyang Yang, Lajos Hanzo

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology.

Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization

no code implementations28 Oct 2021 Dong Liu, Jiankang Zhang, Jingjing Cui, Soon-Xin Ng, Robert G. Maunder, Lajos Hanzo

Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.

End-to-End Image Compression with Probabilistic Decoding

no code implementations30 Sep 2021 Haichuan Ma, Dong Liu, Cunhui Dong, Li Li, Feng Wu

However, this nature was seldom considered in previous studies on image compression, which usually chose one possible image as reconstruction, e. g. the one with the maximal a posteriori probability.

Image Compression

Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining

1 code implementation ICLR 2022 Lu Miao, Xiaolong Luo, Tianlong Chen, Wuyang Chen, Dong Liu, Zhangyang Wang

Conventional methods often require (iterative) pruning followed by re-training, which not only incurs large overhead beyond the original DNN training but also can be sensitive to retraining hyperparameters.

iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform

no code implementations18 Sep 2021 Dongmei Xue, Haichuan Ma, Li Li, Dong Liu, Zhiwei Xiong

With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods.

Image Compression

CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noise

1 code implementation1 Aug 2021 Zeyuan Chen, Yifan Jiang, Dong Liu, Zhangyang Wang

We present \underline{C}oordinated \underline{E}nhancement for \underline{R}eal-world \underline{L}ow-light Noisy Images (CERL), that seamlessly integrates light enhancement and noise suppression parts into a unified and physics-grounded optimization framework.


Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with Explainability

1 code implementation1 Jul 2021 Anubhab Ghosh, Antoine Honoré, Dong Liu, Gustav Eje Henter, Saikat Chatterjee

For a standard speech phone classification setup involving 39 phones (classes) and the TIMIT dataset, we show that the use of standard features called mel-frequency-cepstral-coeffcients (MFCCs), the proposed generative models, and the decision fusion together can achieve $86. 6\%$ accuracy by generative training only.


Light Field Super-Resolution With Zero-Shot Learning

no code implementations CVPR 2021 Zhen Cheng, Zhiwei Xiong, Chang Chen, Dong Liu, Zheng-Jun Zha

To fill this gap, we propose a zero-shot learning framework for light field SR, which learns a mapping to super-resolve the reference view with examples extracted solely from the input low-resolution light field itself.

Super-Resolution Zero-Shot Learning

Structured Multi-Level Interaction Network for Video Moment Localization via Language Query

no code implementations CVPR 2021 Hao Wang, Zheng-Jun Zha, Liang Li, Dong Liu, Jiebo Luo

In particular, for cross-modal interaction, we interact the sentence-level query with the whole moment while interact the word-level query with content and boundary, as in a coarse-to-fine manner.

Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification

no code implementations7 May 2021 Jiawei Liu, Zhipeng Huang, Kecheng Zheng, Dong Liu, Xiaoyan Sun, Zheng-Jun Zha

It describes unseen target domain as a combination of the known source ones, and explicitly learns domain-specific representation with target distribution to improve the model's generalization by a meta-learning pipeline.

Generalizable Person Re-identification Meta-Learning

Simultaneous Navigation and Construction Benchmarking Environments

1 code implementation31 Mar 2021 Wenyu Han, Chen Feng, Haoran Wu, Alexander Gao, Armand Jordana, Dong Liu, Lerrel Pinto, Ludovic Righetti

We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design.

Benchmarking Reinforcement Learning (RL) +2

Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE

2 code implementations CVPR 2021 Jialun Peng, Dong Liu, Songcen Xu, Houqiang Li

We propose a two-stage model for diverse inpainting, where the first stage generates multiple coarse results each of which has a different structure, and the second stage refines each coarse result separately by augmenting texture.

Image Inpainting Quantization +1

Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting

no code implementations24 Feb 2021 Shunxin Xu, Ke Sun, Dong Liu, Zhiwei Xiong, Zheng-Jun Zha

We observe that not only denoising helps combat the drop of segmentation accuracy due to noise, but also pixel-wise semantic information boosts the capability of denoising.

Image Denoising Semantic Segmentation

An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values

no code implementations14 Dec 2020 Danny Smyl, Tyler N. Tallman, Dong Liu, Andreas Hauptmann

Here we present a highly efficient data-driven Quasi-Newton method applicable to nonlinear inverse problems.

Learning Trailer Moments in Full-Length Movies

no code implementations19 Aug 2020 Lezi Wang, Dong Liu, Rohit Puri, Dimitris N. Metaxas

A movie's key moments stand out of the screenplay to grab an audience's attention and make movie browsing efficient.

Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation

5 code implementations Interspeech 2020 Jingjing Chen, Qirong Mao, Dong Liu

By introduces a improved transformer, elements in speech sequences can interact directly, which enables DPTNet can model for the speech sequences with direct context-awareness.

Speech Separation Audio and Speech Processing Sound

Graph Neural Networks for Massive MIMO Detection

1 code implementation11 Jul 2020 Andrea Scotti, Nima N. Moghadam, Dong Liu, Karl Gafvert, Jinliang Huang

In this paper, we innovately use graph neural networks (GNNs) to learn a message-passing solution for the inference task of massive multiple multiple-input multiple-output (MIMO) detection in wireless communication.

$α$ Belief Propagation for Approximate Inference

1 code implementation27 Jun 2020 Dong Liu, Minh Thành Vu, Zuxing Li, Lars K. Rasmussen

To gain a better understanding of BP in general graphs, we derive an interpretable belief propagation algorithm that is motivated by minimization of a localized $\alpha$-divergence.

Efficient Integer-Arithmetic-Only Convolutional Neural Networks

1 code implementation21 Jun 2020 Hengrui Zhao, Dong Liu, Houqiang Li

Considering the tradeoff between activation quantization error and network learning ability, we set an empirical rule to tune the bound of each Bounded ReLU.

Image Super-Resolution Quantization

Foreground-Background Imbalance Problem in Deep Object Detectors: A Review

no code implementations16 Jun 2020 Joya Chen, Qi Wu, Dong Liu, Tong Xu

Recent years have witnessed the remarkable developments made by deep learning techniques for object detection, a fundamentally challenging problem of computer vision.

object-detection Object Detection

Transferring and Regularizing Prediction for Semantic Segmentation

no code implementations CVPR 2020 Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, Tao Mei

In the view of extremely expensive expert labeling, recent research has shown that the models trained on photo-realistic synthetic data (e. g., computer games) with computer-generated annotations can be adapted to real images.

Domain Adaptation Semantic Segmentation

M-LVC: Multiple Frames Prediction for Learned Video Compression

1 code implementation CVPR 2020 Jianping Lin, Dong Liu, Houqiang Li, Feng Wu

To compensate for the compression error of the auto-encoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple reference frames as well.


Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

no code implementations21 Mar 2020 Dong Liu, Jianyu Zhao, Chenyang Yang, Lajos Hanzo

Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning.

Is There Tradeoff between Spatial and Temporal in Video Super-Resolution?

no code implementations13 Mar 2020 Haochen Zhang, Dong Liu, Zhiwei Xiong

Recent advances of deep learning lead to great success of image and video super-resolution (SR) methods that are based on convolutional neural networks (CNN).

Video Super-Resolution

Dual Temporal Memory Network for Efficient Video Object Segmentation

no code implementations13 Mar 2020 Kaihua Zhang, Long Wang, Dong Liu, Bo Liu, Qingshan Liu, Zhu Li

We present an end-to-end network which stores short- and long-term video sequence information preceding the current frame as the temporal memories to address the temporal modeling in VOS.

One-shot visual object segmentation Semantic Segmentation +2

On Dominant Interference in Random Networks and Communication Reliability

1 code implementation3 Mar 2020 Dong Liu, Baptiste Cavarec, Lars K. Rasmussen, Jing Yue

In this paper, we study the characteristics of dominant interference power with directional reception in a random network modelled by a Poisson Point Process.

Information Theory Signal Processing Information Theory

Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning

no code implementations3 Jan 2020 Dong Liu, Chengjian Sun, Chenyang Yang, Lajos Hanzo

If the objective and constraint functions are unavailable, reinforcement learning can be applied to find the solution of a functional optimization problem, which is however not tailored to optimization problems in wireless networks.

Hidden Markov Models for sepsis detection in preterm infants

no code implementations30 Oct 2019 Antoine Honore, Dong Liu, David Forsberg, Karen Coste, Eric Herlenius, Saikat Chatterjee, Mikael Skoglund

We explore the use of traditional and contemporary hidden Markov models (HMMs) for sequential physiological data analysis and sepsis prediction in preterm infants.


Optimizing electrode positions in 2D Electrical Impedance Tomography using deep learning

no code implementations21 Oct 2019 Danny Smyl, Dong Liu

Further, it is found that the use of optimized electrode positions computed using the approach derived herein can reduce errors in EIT reconstructions as well as improve the distinguishability of EIT measurements.

Powering Hidden Markov Model by Neural Network based Generative Models

1 code implementation13 Oct 2019 Dong Liu, Antoine Honoré, Saikat Chatterjee, Lars K. Rasmussen

In the proposed GenHMM, each HMM hidden state is associated with a neural network based generative model that has tractability of exact likelihood and provides efficient likelihood computation.

Is Heuristic Sampling Necessary in Training Deep Object Detectors?

13 code implementations11 Sep 2019 Joya Chen, Dong Liu, Tong Xu, Shiwei Wu, Yifei Cheng, Enhong Chen

In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors.

General Classification Instance Segmentation +1

A Comprehensive Benchmark for Single Image Compression Artifacts Reduction

no code implementations9 Sep 2019 Jiaying Liu, Dong Liu, Wenhan Yang, Sifeng Xia, Xiaoshuai Zhang, Yuanying Dai

We present a comprehensive study and evaluation of existing single image compression artifacts removal algorithms, using a new 4K resolution benchmark including diversified foreground objects and background scenes with rich structures, called Large-scale Ideal Ultra high definition 4K (LIU4K) benchmark.

Image Compression Quantization

Residual Objectness for Imbalance Reduction

no code implementations24 Aug 2019 Joya Chen, Dong Liu, Bin Luo, Xuezheng Peng, Tong Xu, Enhong Chen

For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds.

$α$ Belief Propagation as Fully Factorized Approximation

no code implementations23 Aug 2019 Dong Liu, Nima N. Moghadam, Lars K. Rasmussen, Jinliang Huang, Saikat Chatterjee

Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited.

Deep High-Resolution Representation Learning for Visual Recognition

40 code implementations20 Aug 2019 Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.

 Ranked #1 on Object Detection on COCO test-dev (Hardware Burden metric)

Dichotomous Image Segmentation Face Alignment +7

Model-Free Unsupervised Learning for Optimization Problems with Constraints

no code implementations30 Jul 2019 Chengjian Sun, Dong Liu, Chenyang Yang

In many optimization problems in wireless communications, the expressions of objective function or constraints are hard or even impossible to derive, which makes the solutions difficult to find.

reinforcement-learning Reinforcement Learning (RL)

Composition-Aware Image Aesthetics Assessment

no code implementations25 Jul 2019 Dong Liu, Rohit Puri, Nagendra Kamath, Subhabrata Bhattachary

In this work, we propose to model the image composition information as the mutual dependency of its local regions, and design a novel architecture to leverage such information to boost the performance of aesthetics assessment.

Aesthetics Quality Assessment Image Retrieval +2

SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials

no code implementations17 May 2019 Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi, Shumpei Kikuta, Dong Liu, Partha P. Mitra, Mikael Skoglund

We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices.

Image Classification

Deep Learning-Based Video Coding: A Review and A Case Study

1 code implementation29 Apr 2019 Dong Liu, Yue Li, Jianping Lin, Houqiang Li, Feng Wu

For deep schemes, pixel probability modeling and auto-encoder are the two approaches, that can be viewed as predictive coding scheme and transform coding scheme, respectively.

Multimedia Image and Video Processing

On The Classification-Distortion-Perception Tradeoff

no code implementations NeurIPS 2019 Dong Liu, Haochen Zhang, Zhiwei Xiong

In this paper, we extend the previous perception-distortion tradeoff to the case of classification-distortion-perception (CDP) tradeoff, where we introduced the classification error rate of the restored signal in addition to distortion and perceptual difference.

Classification General Classification

Two-Stream Action Recognition-Oriented Video Super-Resolution

1 code implementation ICCV 2019 Haochen Zhang, Dong Liu, Zhiwei Xiong

Tailored for two-stream action recognition networks, we propose two video SR methods for the spatial and temporal streams respectively.

Action Recognition Optical Flow Estimation +3

Deep High-Resolution Representation Learning for Human Pose Estimation

38 code implementations CVPR 2019 Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang

We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel.

2D Human Pose Estimation Instance Segmentation +6

Entropy-regularized Optimal Transport Generative Models

no code implementations16 Nov 2018 Dong Liu, Minh Thành Vu, Saikat Chatterjee, Lars K. Rasmussen

We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions.

Image Generation

DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification

2 code implementations29 Aug 2018 Xiaofeng Zhang, Zhangyang Wang, Dong Liu, Qing Ling

Given insufficient data, while many techniques have been developed to help combat overfitting, the challenge remains if one tries to train deep networks, especially in the ill-posed extremely low data regimes: only a small set of labeled data are available, and nothing -- including unlabeled data -- else.

Data Augmentation General Classification +1

Fully Convolutional Adaptation Networks for Semantic Segmentation

no code implementations CVPR 2018 Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, Tao Mei

The recent advances in deep neural networks have convincingly demonstrated high capability in learning vision models on large datasets.

Domain Adaptation Semantic Segmentation

Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding

1 code implementation28 Feb 2018 Dong Liu, Ke Sun, Zhangyang Wang, Runsheng Liu, Zheng-Jun Zha

We propose an interpretable deep structure namely Frank-Wolfe Network (F-W Net), whose architecture is inspired by unrolling and truncating the Frank-Wolfe algorithm for solving an $L_p$-norm constrained problem with $p\geq 1$.

Handwritten Digit Recognition Image Denoising +2

A Learning-based Approach to Joint Content Caching and Recommendation at Base Stations

no code implementations22 Jan 2018 Dong Liu, Chenyang Yang

We then formulate a joint caching and recommendation problem maximizing the successful offloading probability, which is a mixed integer programming problem.

Human Pose Estimation using Global and Local Normalization

no code implementations ICCV 2017 Ke Sun, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Dong Liu, Jingdong Wang

We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation.

Pose Estimation

Neural network-based arithmetic coding of intra prediction modes in HEVC

no code implementations18 Sep 2017 Rui Song, Dong Liu, Houqiang Li, Feng Wu

In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC.


Snapshot Hyperspectral Light Field Imaging

no code implementations CVPR 2017 Zhiwei Xiong, Lizhi Wang, Huiqun Li, Dong Liu, Feng Wu

This paper presents the first snapshot hyperspectral light field imager in practice.

A Convolutional Neural Network Approach for Half-Pel Interpolation in Video Coding

no code implementations10 Mar 2017 Ning Yan, Dong Liu, Houqiang Li, Feng Wu

To further improve the coding efficiency, sub-pel motion compensation has been utilized, which requires interpolation of fractional samples.


Convolutional Neural Network-Based Block Up-sampling for Intra Frame Coding

no code implementations22 Feb 2017 Yue Li, Dong Liu, Houqiang Li, Li Li, Feng Wu, Hong Zhang, Haitao Yang

A block can be down-sampled before being compressed by normal intra coding, and then up-sampled to its original resolution.


A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding

1 code implementation24 Aug 2016 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.


Comparative Deep Learning of Hybrid Representations for Image Recommendations

no code implementations CVPR 2016 Chenyi Lei, Dong Liu, Weiping Li, Zheng-Jun Zha, Houqiang Li

In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations.

EventNet: A Large Scale Structured Concept Library for Complex Event Detection in Video

no code implementations8 Jun 2015 Guangnan Ye, Yitong Li, Hongliang Xu, Dong Liu, Shih-Fu Chang

Extensive experiments over the zero-shot event retrieval task when no training samples are available show that the EventNet concept library consistently and significantly outperforms the state-of-the-art (such as the 20K ImageNet concepts trained with CNN) by a large margin up to 207%.

Event Detection Retrieval

Building A Large Concept Bank for Representing Events in Video

no code implementations29 Mar 2014 Yin Cui, Dong Liu, Jiawei Chen, Shih-Fu Chang

In this paper, we propose to build Concept Bank, the largest concept library consisting of 4, 876 concepts specifically designed to cover 631 real-world events.

Event Detection Retrieval

$\propto$SVM for learning with label proportions

no code implementations4 Jun 2013 Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang

We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known.

Sample-Specific Late Fusion for Visual Category Recognition

no code implementations CVPR 2013 Dong Liu, Kuan-Ting Lai, Guangnan Ye, Ming-Syan Chen, Shih-Fu Chang

However, the existing methods generally use a fixed fusion weight for all the scores of a classifier, and thus fail to optimally determine the fusion weight for the individual samples.

Robust Object Co-detection

no code implementations CVPR 2013 Xin Guo, Dong Liu, Brendan Jou, Mojun Zhu, Anni Cai, Shih-Fu Chang

Object co-detection aims at simultaneous detection of objects of the same category from a pool of related images by exploiting consistent visual patterns present in candidate objects in the images.

Clustering object-detection +1

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