Search Results for author: Ning Xu

Found 84 papers, 32 papers with code

Self-Attention Graph Residual Convolutional Networks for Event Detection with dependency relations

no code implementations Findings (EMNLP) 2021 AnAn Liu, Ning Xu, Haozhe Liu

While existing GCN-based methods explore latent node-to-node dependency relations according to a stationary adjacency tensor, an attention-based dynamic tensor, which can pay much attention to the key node like event trigger or its neighboring nodes, has not been developed.

Event Detection

Variational Label Enhancement

no code implementations ICML 2020 Ning Xu, Yun-Peng Liu, Jun Shu, Xin Geng

Label distribution covers a certain number of labels, representing the degree to which each label describes the instance.

Multi-Label Learning Variational Inference

Causality is all you need

no code implementations21 Nov 2023 Ning Xu, YiFei Gao, Hongshuo Tian, Yongdong Zhang, An-An Liu

In this paper, we propose the Causal Graph Routing (CGR) framework, an integrated causal scheme relying entirely on the intervention mechanisms to reveal the cause-effect forces hidden in data.

Document Classification

Can Class-Priors Help Single-Positive Multi-Label Learning?

no code implementations25 Sep 2023 Biao Liu, Jie Wang, Ning Xu, Xin Geng

Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label.

Multi-Label Learning

T2IW: Joint Text to Image & Watermark Generation

no code implementations7 Sep 2023 An-An Liu, Guokai Zhang, Yuting Su, Ning Xu, Yongdong Zhang, Lanjun Wang

Furthermore, we strengthen the watermark robustness of our approach by subjecting the compound image to various post-processing attacks, with minimal pixel distortion observed in the revealed watermark.

Image Generation

Variational Label-Correlation Enhancement for Congestion Prediction

no code implementations1 Aug 2023 Biao Liu, Congyu Qiao, Ning Xu, Xin Geng, Ziran Zhu, Jun Yang

In order to fully exploit the inherent spatial label-correlation between neighboring grids, we propose a novel approach, {\ours}, i. e., VAriational Label-Correlation Enhancement for Congestion Prediction, which considers the local label-correlation in the congestion map, associating the estimated congestion value of each grid with a local label-correlation weight influenced by its surrounding grids.

Variational Inference

Towards Effective Visual Representations for Partial-Label Learning

1 code implementation CVPR 2023 Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, Xin Geng

Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning has recently boosted the performance of PLL on vision tasks, attributed to representations learned by contrasting the same/different classes of entities.

Contrastive Learning Image Classification +3

Scalable Multiple Patterning Layout Decomposition Implemented by a Distribution Evolutionary Algorithm

no code implementations9 Apr 2023 Yu Chen, Yongjian Xu, Ning Xu

As the feature size of semiconductor technology shrinks to 10 nm and beyond, the multiple patterning lithography (MPL) attracts more attention from the industry.

Unreliable Partial Label Learning with Recursive Separation

no code implementations20 Feb 2023 Yu Shi, Ning Xu, Hua Yuan, Xin Geng

Therefore, a generalized PLL named Unreliable Partial Label Learning (UPLL) is proposed, in which the true label may not be in the candidate label set.

Partial Label Learning Weakly-supervised Learning

Learning From Biased Soft Labels

no code implementations16 Feb 2023 Hua Yuan, Ning Xu, Yu Shi, Xin Geng, Yong Rui

We present two more comprehensive indicators to measure the effectiveness of such soft labels.

Knowledge Distillation

Progressive Purification for Instance-Dependent Partial Label Learning

no code implementations2 Jun 2022 Ning Xu, Biao Liu, Jiaqi Lv, Congyu Qiao, Xin Geng

Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct.

Partial Label Learning

One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement

1 code implementation1 Jun 2022 Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang

To cope with the challenge, we investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label, and show that one can successfully learn a theoretically grounded multi-label classifier for the problem.

Multi-Label Learning

Decompositional Generation Process for Instance-Dependent Partial Label Learning

1 code implementation8 Apr 2022 Congyu Qiao, Ning Xu, Xin Geng

Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way.

Partial Label Learning Weakly-supervised Learning

A Distribution Evolutionary Algorithm for the Graph Coloring Problem

no code implementations29 Mar 2022 Yongjian Xu, Huabin Cheng, Ning Xu, Yu Chen, Chengwang Xie

Unlike existing estimation of distribution algorithms where a probability model is updated by generated solutions, DEA-PPM employs a distribution population based on a novel probability model, and an orthogonal exploration strategy is introduced to search the distribution space with the assistance of an refinement strategy.

Combinatorial Optimization

CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training

1 code implementation22 Mar 2022 Haitian Zheng, Zhe Lin, Jingwan Lu, Scott Cohen, Eli Shechtman, Connelly Barnes, Jianming Zhang, Ning Xu, Sohrab Amirghodsi, Jiebo Luo

We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level.

Image Inpainting

Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation

no code implementations CVPR 2022 Linfeng Zhang, Xin Chen, Xiaobing Tu, Pengfei Wan, Ning Xu, Kaisheng Ma

Instead of directly distilling the generated images of teachers, wavelet knowledge distillation first decomposes the images into different frequency bands with discrete wavelet transformation and then only distills the high frequency bands.

Image-to-Image Translation Knowledge Distillation +1

End-to-end video instance segmentation via spatial-temporal graph neural networks

1 code implementation ICCV 2021 Tao Wang, Ning Xu, Kean Chen, Weiyao Lin

Specifically, graph nodes representing instance features are used for detection and segmentation while graph edges representing instance relations are used for tracking.

Instance Segmentation Segmentation +2

SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing

no code implementations CVPR 2022 Jing Shi, Ning Xu, Haitian Zheng, Alex Smith, Jiebo Luo, Chenliang Xu

Recently, large pretrained models (e. g., BERT, StyleGAN, CLIP) show great knowledge transfer and generalization capability on various downstream tasks within their domains.

Image-to-Image Translation Retrieval +1

SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing

no code implementations30 Nov 2021 Jing Shi, Ning Xu, Haitian Zheng, Alex Smith, Jiebo Luo, Chenliang Xu

Recently, large pretrained models (e. g., BERT, StyleGAN, CLIP) have shown great knowledge transfer and generalization capability on various downstream tasks within their domains.

Image-to-Image Translation Retrieval +1

Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective

1 code implementation2 Nov 2021 Yuxi Li, Ning Xu, Wenjie Yang, John See, Weiyao Lin

We conduct comprehensive comparison and detailed analysis on challenging benchmarks of DAVIS16, DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is helpful to enhance segmentation quality, improve the robustness of VOS systems, and further provide qualitative comparison and interpretation on how different VOS algorithms work.

Segmentation Semantic Segmentation +2

Instance-Dependent Partial Label Learning

1 code implementation NeurIPS 2021 Ning Xu, Congyu Qiao, Xin Geng, Min-Ling Zhang

In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature.

Partial Label Learning Weakly-supervised Learning

TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

1 code implementation12 Aug 2021 Jinyu Yang, Jingjing Liu, Ning Xu, Junzhou Huang

With the recent exponential increase in applying Vision Transformer (ViT) to vision tasks, the capability of ViT in adapting cross-domain knowledge, however, remains unexplored in the literature.

Transfer Learning Unsupervised Domain Adaptation

Learning by Planning: Language-Guided Global Image Editing

1 code implementation CVPR 2021 Jing Shi, Ning Xu, Yihang Xu, Trung Bui, Franck Dernoncourt, Chenliang Xu

Recently, language-guided global image editing draws increasing attention with growing application potentials.

Learngene: From Open-World to Your Learning Task

1 code implementation12 Jun 2021 Qiufeng Wang, Xin Geng, Shuxia Lin, Shiyu Xia, Lei Qi, Ning Xu

Moreover, the learngene, i. e., the gene for learning initialization rules of the target model, is proposed to inherit the meta-knowledge from the collective model and reconstruct a lightweight individual model on the target task.

On the Robustness of Average Losses for Partial-Label Learning

no code implementations11 Jun 2021 Jiaqi Lv, Biao Liu, Lei Feng, Ning Xu, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama

Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL).

Partial Label Learning Weakly Supervised Classification

A Simple Baseline for Weakly-Supervised Scene Graph Generation

no code implementations ICCV 2021 Jing Shi, Yiwu Zhong, Ning Xu, Yin Li, Chenliang Xu

We investigate the weakly-supervised scene graph generation, which is a challenging task since no correspondence of label and object is provided.

Contrastive Learning Graph Generation +2

Language-Guided Global Image Editing via Cross-Modal Cyclic Mechanism

no code implementations ICCV 2021 Wentao Jiang, Ning Xu, Jiayun Wang, Chen Gao, Jing Shi, Zhe Lin, Si Liu

Given the cycle, we propose several free augmentation strategies to help our model understand various editing requests given the imbalanced dataset.

Semantic Layout Manipulation with High-Resolution Sparse Attention

1 code implementation14 Dec 2020 Haitian Zheng, Zhe Lin, Jingwan Lu, Scott Cohen, Jianming Zhang, Ning Xu, Jiebo Luo

A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic.

Vocal Bursts Intensity Prediction

Mask Guided Matting via Progressive Refinement Network

1 code implementation CVPR 2021 Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe Lin, Ning Xu, Yutong Bai, Alan Yuille

We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance.

Image Matting

A Benchmark and Baseline for Language-Driven Image Editing

no code implementations5 Oct 2020 Jing Shi, Ning Xu, Trung Bui, Franck Dernoncourt, Zheng Wen, Chenliang Xu

To solve this new task, we first present a new language-driven image editing dataset that supports both local and global editing with editing operation and mask annotations.

Compact Learning for Multi-Label Classification

no code implementations18 Sep 2020 Jiaqi Lv, Tianran Wu, Chenglun Peng, Yun-Peng Liu, Ning Xu, Xin Geng

In this paper, we present a compact learning (CL) framework to embed the features and labels simultaneously and with mutual guidance.

Classification Dimensionality Reduction +3

Finding Action Tubes with a Sparse-to-Dense Framework

no code implementations30 Aug 2020 Yuxi Li, Weiyao Lin, Tao Wang, John See, Rui Qian, Ning Xu, Li-Min Wang, Shugong Xu

The task of spatial-temporal action detection has attracted increasing attention among researchers.

Ranked #3 on Action Detection on UCF Sports (Video-mAP 0.2 metric)

Action Detection

CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization

no code implementations ECCV 2020 Yuxi Li, Weiyao Lin, John See, Ning Xu, Shugong Xu, Ke Yan, Cong Yang

Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization.

Action Detection Spatio-Temporal Action Localization +1

Accuracy and stability of solar variable selection comparison under complicated dependence structures

no code implementations30 Jul 2020 Ning Xu, Timothy C. G. Fisher, Jian Hong

In this paper we focus on the empirical variable-selection peformance of subsample-ordered least angle regression (Solar) -- a novel ultrahigh dimensional redesign of lasso -- on the empirical data with complicated dependence structures and, hence, severe multicollinearity and grouping effect issues.

Graph Learning regression +1

Rademacher upper bounds for cross-validation errors with an application to the lasso

no code implementations30 Jul 2020 Ning Xu, Timothy C. G. Fisher, Jian Hong

We establish a general upper bound for $K$-fold cross-validation ($K$-CV) errors that can be adapted to many $K$-CV-based estimators and learning algorithms.

Blocking Variable Selection

Solar: $L_0$ solution path averaging for fast and accurate variable selection in high-dimensional data

1 code implementation30 Jul 2020 Ning Xu, Timothy C. G. Fisher

We propose a new variable selection algorithm, subsample-ordered least-angle regression (solar), and its coordinate descent generalization, solar-cd.

Variable Selection

Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing

no code implementations30 Jul 2020 Ning Xu, Timothy C. G. Fisher, Jian Hong

In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney.

BIG-bench Machine Learning Econometrics +4

Incorporating Reinforced Adversarial Learning in Autoregressive Image Generation

no code implementations ECCV 2020 Kenan E. Ak, Ning Xu, Zhe Lin, Yilin Wang

To our best knowledge, the proposed method is first to enable adversarial learning in autoregressive models for image generation.

Image Generation

Multiple Sound Sources Localization from Coarse to Fine

1 code implementation ECCV 2020 Rui Qian, Di Hu, Heinrich Dinkel, Mengyue Wu, Ning Xu, Weiyao Lin

How to visually localize multiple sound sources in unconstrained videos is a formidable problem, especially when lack of the pairwise sound-object annotations.

AOWS: Adaptive and optimal network width search with latency constraints

1 code implementation CVPR 2020 Maxim Berman, Leonid Pishchulin, Ning Xu, Matthew B. Blaschko, Gerard Medioni

We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware.

Neural Architecture Search

Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events

no code implementations9 May 2020 Weiyao Lin, Huabin Liu, Shizhan Liu, Yuxi Li, Rui Qian, Tao Wang, Ning Xu, Hongkai Xiong, Guo-Jun Qi, Nicu Sebe

To this end, we present a new large-scale dataset with comprehensive annotations, named Human-in-Events or HiEve (Human-centric video analysis in complex Events), for the understanding of human motions, poses, and actions in a variety of realistic events, especially in crowd & complex events.

Action Recognition Pose Estimation

Minimizing FLOPs to Learn Efficient Sparse Representations

1 code implementation ICLR 2020 Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos

Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification.

Quantization Representation Learning +1

Channel Attention Is All You Need for Video Frame Interpolation

1 code implementation AAAI Conference on Artificial Intelligence 2020 Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, Kyoung Mu Lee

Prevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion.

Motion Estimation Optical Flow Estimation +1

Getting to 99% Accuracy in Interactive Segmentation

3 code implementations17 Mar 2020 Marco Forte, Brian Price, Scott Cohen, Ning Xu, François Pitié

We propose a novel interactive architecture and a novel training scheme that are both tailored to better exploit the user workflow.

Interactive Segmentation

MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention

no code implementations18 Feb 2020 Donghyun Kim, Tian Lan, Chuhang Zou, Ning Xu, Bryan A. Plummer, Stan Sclaroff, Jayan Eledath, Gerard Medioni

We embed the attention module in a ``slow-fast'' architecture, where the slower network runs on sparsely sampled keyframes and the light-weight shallow network runs on non-keyframes at a high frame rate.

Multi-Task Learning

An Internal Learning Approach to Video Inpainting

1 code implementation ICCV 2019 Haotian Zhang, Long Mai, Ning Xu, Zhaowen Wang, John Collomosse, Hailin Jin

We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in static images.

Optical Flow Estimation Video Inpainting

Large-scale Tag-based Font Retrieval with Generative Feature Learning

no code implementations ICCV 2019 Tianlang Chen, Zhaowen Wang, Ning Xu, Hailin Jin, Jiebo Luo

In this paper, we address the problem of large-scale tag-based font retrieval which aims to bring semantics to the font selection process and enable people without expert knowledge to use fonts effectively.

Retrieval TAG

Controllable Artistic Text Style Transfer via Shape-Matching GAN

1 code implementation ICCV 2019 Shuai Yang, Zhangyang Wang, Zhaowen Wang, Ning Xu, Jiaying Liu, Zongming Guo

In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter.

Style Transfer Text Style Transfer

Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks

1 code implementation CVPR 2019 Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim

We propose a new multi-round training scheme for the interactive video object segmentation so that the networks can learn how to understand the user's intention and update incorrect estimations during the training.

Interactive Video Object Segmentation Segmentation +2

Streamlined Dense Video Captioning

1 code implementation CVPR 2019 Jonghwan Mun, Linjie Yang, Zhou Ren, Ning Xu, Bohyung Han

Dense video captioning is an extremely challenging task since accurate and coherent description of events in a video requires holistic understanding of video contents as well as contextual reasoning of individual events.

Dense Video Captioning

M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning

no code implementations3 Apr 2019 Peng Zhou, Long Mai, Jianming Zhang, Ning Xu, Zuxuan Wu, Larry S. Davis

Instead of sequentially distilling knowledge only from the last model, we directly leverage all previous model snapshots.

Incremental Learning Knowledge Distillation

End-to-End Time-Lapse Video Synthesis from a Single Outdoor Image

no code implementations CVPR 2019 Seonghyeon Nam, Chongyang Ma, Menglei Chai, William Brendel, Ning Xu, Seon Joo Kim

Time-lapse videos usually contain visually appealing content but are often difficult and costly to create.

Video Object Segmentation using Space-Time Memory Networks

3 code implementations ICCV 2019 Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim

In our framework, the past frames with object masks form an external memory, and the current frame as the query is segmented using the mask information in the memory.

Ranked #4 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)

Interactive Video Object Segmentation One-shot visual object segmentation +2

Singing voice conversion with non-parallel data

no code implementations11 Mar 2019 Xin Chen, Wei Chu, Jinxi Guo, Ning Xu

F0 and aperiodic are obtained through the original singing voice, and used with acoustic features to reconstruct the target singing voice through a vocoder.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Slimmable Neural Networks

3 code implementations ICLR 2019 Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang

Instead of training individual networks with different width configurations, we train a shared network with switchable batch normalization.

Instance Segmentation Keypoint Detection +3

Deep neural network based i-vector mapping for speaker verification using short utterances

no code implementations16 Oct 2018 Jinxi Guo, Ning Xu, Kailun Qian, Yang Shi, Kaiyuan Xu, Ying-Nian Wu, Abeer Alwan

Experimental results using the NIST SRE 2010 dataset show that both methods provide significant improvement and result in a max of 28. 43% relative improvement in Equal Error Rates from a baseline system, when using deep encoder with residual blocks and adding an additional phoneme vector.

Speaker Recognition Speaker Verification +1

YouTube-VOS: Sequence-to-Sequence Video Object Segmentation

4 code implementations ECCV 2018 Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, Thomas Huang

End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.

Ranked #16 on Video Object Segmentation on YouTube-VOS 2018 (F-Measure (Unseen) metric)

Image Segmentation One-shot visual object segmentation +7

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch

no code implementations CVPR 2019 Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran

In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain.


Factorized Adversarial Networks for Unsupervised Domain Adaptation

no code implementations4 Jun 2018 Jian Ren, Jianchao Yang, Ning Xu, David J. Foran

In this paper, we propose Factorized Adversarial Networks (FAN) to solve unsupervised domain adaptation problems for image classification tasks.

General Classification Image Classification +1

Learn to Combine Modalities in Multimodal Deep Learning

1 code implementation29 May 2018 Kuan Liu, Yanen Li, Ning Xu, Prem Natarajan

Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches.

Multimodal Deep Learning

Learning $3$D-FilterMap for Deep Convolutional Neural Networks

no code implementations5 Jan 2018 Yingzhen Yang, Jianchao Yang, Ning Xu, Wei Han

Due to the weight sharing scheme, the parameter size of the $3$D-FilterMap is much smaller than that of the filters to be learned in the conventional convolution layer when $3$D-FilterMap generates the same number of filters.

Deep GrabCut for Object Selection

no code implementations2 Jul 2017 Ning Xu, Brian Price, Scott Cohen, Jimei Yang, Thomas Huang

In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map.

Instance Segmentation Interactive Segmentation +2

Multi-Label Learning with Label Enhancement

no code implementations26 Jun 2017 Ruifeng Shao, Ning Xu, Xin Geng

To solve this problem, we assume that each multi-label instance is described by a vector of latent real-valued labels, which can reflect the importance of the corresponding labels.

Multi-Label Learning

$\left( β, \varpi \right)$-stability for cross-validation and the choice of the number of folds

no code implementations20 May 2017 Ning Xu, Jian Hong, Timothy C. G. Fisher

The $\left( \beta, \varpi \right)$-stability mathematically connects the generalization ability and the stability of the cross-validated model via the Rademacher complexity.

Model Selection Test

Deep Image Matting

8 code implementations CVPR 2017 Ning Xu, Brian Price, Scott Cohen, Thomas Huang

We evaluate our algorithm on the image matting benchmark, our testing set, and a wide variety of real images.

Semantic Image Matting

Finite-sample and asymptotic analysis of generalization ability with an application to penalized regression

no code implementations12 Sep 2016 Ning Xu, Jian Hong, Timothy C. G. Fisher

We show that the error bounds may be used for tuning key estimation hyper-parameters, such as the number of folds $K$ in cross-validation.


Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso

no code implementations1 Jun 2016 Ning Xu, Jian Hong, Timothy C. G. Fisher

In this paper, we study model selection from the perspective of generalization ability, under the framework of structural risk minimization (SRM) and Vapnik-Chervonenkis (VC) theory.

Model Selection

Intra-and-Inter-Constraint-based Video Enhancement based on Piecewise Tone Mapping

no code implementations21 Feb 2015 Yuanzhe Chen, Weiyao Lin, Chongyang Zhang, Zhenzhong Chen, Ning Xu, Jun Xie

In this paper, we propose a new intra-and-inter-constraint-based video enhancement approach aiming to 1) achieve high intra-frame quality of the entire picture where multiple region-of-interests (ROIs) can be adaptively and simultaneously enhanced, and 2) guarantee the inter-frame quality consistencies among video frames.

Tone Mapping Video Enhancement

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