Search Results for author: Chen Gong

Found 72 papers, 21 papers with code

数据标注方法比较研究:以依存句法树标注为例(Comparison Study on Data Annotation Approaches: Dependency Tree Annotation as Case Study)

no code implementations CCL 2021 Mingyue Zhou, Chen Gong, Zhenghua Li, Min Zhang

“数据标注最重要的考虑因素是数据的质量和标注代价。我们调研发现自然语言处理领域的数据标注工作通常采用机标人校的标注方法以降低代价;同时, 很少有工作严格对比不同标注方法, 以探讨标注方法对标注质量和代价的影响。该文借助一个成熟的标注团队, 以依存句法数据标注为案例, 实验对比了机标人校、双人独立标注、及本文通过融合前两种方法所新提出的人机独立标注方法, 得到了一些初步的结论。”

A Hierarchical Approach to Population Training for Human-AI Collaboration

1 code implementation26 May 2023 Yi Loo, Chen Gong, Malika Meghjani

A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase.

Hierarchical Reinforcement Learning reinforcement-learning

Can SAM Segment Polyps?

no code implementations15 Apr 2023 Tao Zhou, Yizhe Zhang, Yi Zhou, Ye Wu, Chen Gong

Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks.

Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement Learning

1 code implementation1 Apr 2023 Hao Chen, Chen Gong, Yizhe WANG, Xinwen Hou

This paper proposes the Recovery Triggered States (RTS) method, a novel approach that effectively protects the victim agents from backdoor attacks.

Backdoor Attack reinforcement-learning

Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

1 code implementation CVPR 2023 Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu

Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.

Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals

no code implementations7 Feb 2023 Chen Gong, Yue Chen, Yanan Sui, Luming Li

This sleep stage classification model could be adapted to chronic and continuous monitor sleep for Parkinson's patients in daily life, and potentially utilized for more precise treatment in deep brain-machine interfaces, such as closed-loop deep brain stimulation.

Automatic Sleep Stage Classification Classification +1

Centralized Cooperative Exploration Policy for Continuous Control Tasks

1 code implementation6 Jan 2023 Chao Li, Chen Gong, Qiang He, Xinwen Hou, Yu Liu

To explicitly encourage exploration in continuous control tasks, we propose CCEP (Centralized Cooperative Exploration Policy), which utilizes underestimation and overestimation of value functions to maintain the capacity of exploration.

Continuous Control

Feature Aggregation and Propagation Network for Camouflaged Object Detection

1 code implementation2 Dec 2022 Tao Zhou, Yi Zhou, Chen Gong, Jian Yang, Yu Zhang

In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection.

object-detection Object Detection

Unsupervised Domain Adaptation GAN Inversion for Image Editing

no code implementations22 Nov 2022 Siyu Xing, Chen Gong, Hewei Guo, Xiao-Yu Zhang, Xinwen Hou, Yu Liu

In this paper, we resolve this problem by introducing Unsupervised Domain Adaptation (UDA) into the Inversion process, namely UDA-Inversion, for both high-quality and low-quality image inversion and editing.

Image Reconstruction Unsupervised Domain Adaptation

Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data

no code implementations31 Oct 2022 Lei Zhang, Shilin Zhou, Chen Gong, Zhenghua Li, Zhefeng Wang, Baoxing Huai, Min Zhang

Chinese word segmentation (CWS) models have achieved very high performance when the training data is sufficient and in-domain.

Chinese Word Segmentation

Watermarking for Out-of-distribution Detection

1 code implementation27 Oct 2022 Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han

Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Discourse-Aware Emotion Cause Extraction in Conversations

no code implementations26 Oct 2022 Dexin Kong, Nan Yu, Yun Yuan, Guohong Fu, Chen Gong

In this paper, we investigate the importance of discourse structures in handling utterance interactions and conversationspecific features for ECEC.

Causal Emotion Entailment Discourse Parsing +2

Unifying Graph Contrastive Learning with Flexible Contextual Scopes

1 code implementation17 Oct 2022 Yizhen Zheng, Yu Zheng, Xiaofei Zhou, Chen Gong, Vincent CS Lee, Shirui Pan

To address aforementioned problems, we present a simple self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short).

Contrastive Learning Graph Representation Learning +1

Mind Your Data! Hiding Backdoors in Offline Reinforcement Learning Datasets

1 code implementation7 Oct 2022 Chen Gong, Zhou Yang, Yunpeng Bai, Junda He, Jieke Shi, Arunesh Sinha, Bowen Xu, Xinwen Hou, Guoliang Fan, David Lo

Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack.

Autonomous Driving Backdoor Attack +3

To Store or Not? Online Data Selection for Federated Learning with Limited Storage

no code implementations1 Sep 2022 Chen Gong, Zhenzhe Zheng, Yunfeng Shao, Bingshuai Li, Fan Wu, Guihai Chen

We first define a new data valuation metric for data evaluation and selection in FL with theoretical guarantees for speeding up model convergence and enhancing final model accuracy, simultaneously.

Data Valuation Federated Learning +4

The design and optimization of synchronization sequence for Ultraviolet communication

no code implementations2 Aug 2022 Shihui Yu, Chen Gong, Zhengyuan Xu

Compared with equilong random sequence, the synchronization accuracy of the optimized synchronization sequence is significantly improved.

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style

no code implementations7 Jul 2022 Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention.

Data Augmentation Disentanglement +3

Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels

no code implementations27 Jun 2022 Chuang Zhang, Li Shen, Jian Yang, Chen Gong

To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of DNNs in the early stage of learning to correct noisy labels.

Learning with noisy labels Memorization

Understanding Robust Overfitting of Adversarial Training and Beyond

1 code implementation17 Jun 2022 Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu

Here, we explore the causes of robust overfitting by comparing the data distribution of \emph{non-overfit} (weak adversary) and \emph{overfitted} (strong adversary) adversarial training, and observe that the distribution of the adversarial data generated by weak adversary mainly contain small-loss data.

Adversarial Robustness Data Ablation

Hyperspectral Image Classification With Contrastive Graph Convolutional Network

no code implementations11 May 2022 Wentao Yu, Sheng Wan, Guangyu Li, Jian Yang, Chen Gong

To enhance the feature representation ability, in this paper, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations, which is termed Contrastive Graph Convolutional Network (ConGCN), for HSI classification.

Classification Contrastive Learning +2

Indoor 3-Dimensional Visible Light Positioning: Error Metric and LED Layout Optimization

no code implementations29 Apr 2022 Jiaojiao Xu, Nuo Huang, Chen Gong

We consider 3-dimensional (3D) visible light positioning (VLP) based on smartphone camera in an indoor scenario.

Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation

no code implementations21 Mar 2022 Yongliang Ding, Tao Zhou, Chuang Zhang, Yijing Luo, Juan Tang, Chen Gong

Further, by defining a new form of data centroid, we transform the recovery problem of a label-dependent part to a centroid estimation problem.

Binary Classification

Synergistic Network Learning and Label Correction for Noise-robust Image Classification

no code implementations27 Feb 2022 Chen Gong, Kong Bin, Eric J. Seibel, Xin Wang, Youbing Yin, Qi Song

Taking the expertise of DNNs to learn meaningful patterns before fitting noise, our framework first trains two networks over the current dataset with small loss selection.

Image Classification

Consistency and Diversity induced Human Motion Segmentation

no code implementations10 Feb 2022 Tao Zhou, Huazhu Fu, Chen Gong, Ling Shao, Fatih Porikli, Haibin Ling, Jianbing Shen

Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance.

Motion Segmentation Transfer Learning

Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

1 code implementation9 Dec 2021 Yunpeng Bai, Chen Gong, Bin Zhang, Guoliang Fan, Xinwen Hou, Yu Liu

HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nodes and hyperedges among nodes indicate that the corresponding agents can coordinate to achieve larger rewards.

reinforcement-learning Reinforcement Learning (RL) +4

Universal Semi-Supervised Learning

no code implementations NeurIPS 2021 Zhuo Huang, Chao Xue, Bo Han, Jian Yang, Chen Gong

Universal Semi-Supervised Learning (UniSSL) aims to solve the open-set problem where both the class distribution (i. e., class set) and feature distribution (i. e., feature domain) are different between labeled dataset and unlabeled dataset.

Domain Adaptation

Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels

no code implementations NeurIPS 2021 Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong

Essentially, our CGPN can enhance the learning performance of GNNs under extremely limited labels by contrastively propagating the limited labels to the entire graph.

Graph Attention Node Classification +1

Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding

no code implementations12 Oct 2021 Minnan Luo, Xiaojun Chang, Chen Gong

In this paper, we decompose the video into several segments and intuitively model the task of complex event detection as a multiple instance learning problem by representing each video as a "bag" of segments in which each segment is referred to as an instance.

Event Detection Multiple Instance Learning

Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples

no code implementations29 Sep 2021 Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu

The sample selection approach is popular in learning with noisy labels, which tends to select potentially clean data out of noisy data for robust training.

Learning with noisy labels

The $f$-Divergence Reinforcement Learning Framework

no code implementations24 Sep 2021 Chen Gong, Qiang He, Yunpeng Bai, Zhou Yang, Xiaoyu Chen, Xinwen Hou, Xianjie Zhang, Yu Liu, Guoliang Fan

In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards.

Decision Making Mathematical Proofs +2

MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning

no code implementations22 Sep 2021 Qiang He, Huangyuan Su, Chen Gong, Xinwen Hou

During the training of a reinforcement learning (RL) agent, the distribution of training data is non-stationary as the agent's behavior changes over time.

Gaussian Processes Q-Learning +2

LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders

no code implementations22 Sep 2021 Xiaoyu Chen, Chen Gong, Qiang He, Xinwen Hou, Yu Liu

Variational autoencoders (VAEs), as an important aspect of generative models, have received a lot of research interests and reached many successful applications.

Image Generation

Probabilistic Margins for Instance Reweighting in Adversarial Training

1 code implementation NeurIPS 2021 Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights.

Adversarial Robustness

KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation

1 code implementation11 Jun 2021 Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, William K. Cheung, Bo Han

However, in an open world, the unlabeled test images probably contain unknown categories and have different distributions from the labeled images.

Domain Adaptation Semantic Segmentation

An In-depth Study on Internal Structure of Chinese Words

1 code implementation ACL 2021 Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan

Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.

Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning

1 code implementation12 May 2021 Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan

To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.

Contrastive Learning Graph Representation Learning

Learning with Group Noise

no code implementations17 Mar 2021 Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han

Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels.

Learning with noisy labels

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

1 code implementation27 Feb 2021 Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis

Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.

Anomaly Detection Contrastive Learning +1

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

1 code implementation14 Jan 2021 Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs).

Channel Modeling and Signal Processing for Array-based Visible Light Communication System in Misalignment

no code implementations10 Jan 2021 Jiaqi Wei, Chen Gong, Nuo Huang, Zhengyuan Xu

In this way the light emitted by different LED can be separated well from each other then minimize signal interference.

A prognostic dynamic model applicable to infectious diseases providing easily visualized guides -- A case study of COVID-19 in the UK

no code implementations14 Dec 2020 Yuxuan Zhang, Chen Gong, Dawei Li, Zhi-Wei Wang, Shengda D Pu, Alex W Robertson, Hong Yu, John Parrington

A reasonable prediction of infectious diseases transmission process under different disease control strategies is an important reference point for policy makers.

Multi-grained Chinese Word Segmentation with Weakly Labeled Data

no code implementations COLING 2020 Chen Gong, Zhenghua Li, Bowei Zou, Min Zhang

Detailed evaluation shows that our proposed model with weakly labeled data significantly outperforms the state-of-the-art MWS model by 1. 12 and 5. 97 on NEWS and BAIKE data in F1.

Chinese Word Segmentation

They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning

no code implementations27 Nov 2020 Zhuo Huang, Ying Tai, Chengjie Wang, Jian Yang, Chen Gong

Semi-Supervised Learning (SSL) with mismatched classes deals with the problem that the classes-of-interests in the limited labeled data is only a subset of the classes in massive unlabeled data.

Domain Adaptation

Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification

no code implementations19 Sep 2020 Sheng Wan, Chen Gong, Shirui Pan, Jie Yang, Jian Yang

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification.

General Classification graph construction +2

Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning

no code implementations15 Sep 2020 Sheng Wan, Shirui Pan, Jian Yang, Chen Gong

Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph.

Wireless Communication Based on Microwave Photon-Level Detection With Superconducting Devices: Achievable Rate Prediction

no code implementations25 Jun 2020 Junyu Zhang, Chen Gong, Shangbin Li, Rui Ni, Chengjie Zuo, Jinkang Zhu, Ming Zhao, Zhengyuan Xu

Future wireless communication system embraces physical-layer signal detection with high sensitivity, especially in the microwave photon level.

Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

1 code implementation ICML 2020 Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang

Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i. e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples.

Weak Radio Frequency Signal Detection Based on Piezo-Opto-Electro-Mechanical System: Architecture Design and Sensitivity Prediction

no code implementations29 Mar 2020 Shanchi Wu, Chen Gong, Chengjie Zuo, Shangbin Li, Junyu Zhang, Zhongbin Dai, Kai Yang, Ming Zhao, Rui Ni, Zhengyuan Xu, Jinkang Zhu

We propose a novel radio-frequency (RF) receiving architecture based on micro-electro-mechanical system (MEMS) and optical coherent detection module.

Network Cooperation with Progressive Disambiguation for Partial Label Learning

no code implementations22 Feb 2020 Yao Yao, Chen Gong, Jiehui Deng, Jian Yang

Partial Label Learning (PLL) aims to train a classifier when each training instance is associated with a set of candidate labels, among which only one is correct but is not accessible during the training phase.

Partial Label Learning

Curvilinear Distance Metric Learning

1 code implementation NeurIPS 2019 Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang

To address this issue, we first reveal that the traditional linear distance metric is equivalent to the cumulative arc length between the data pair's nearest points on the learned straight measurer lines.

Metric Learning

Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network

no code implementations26 Sep 2019 Sheng Wan, Chen Gong, Ping Zhong, Shirui Pan, Guangyu Li, Jian Yang

In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance.

Classification General Classification +1

An Approach to Efficient Fitting of Univariate and Multivariate Stochastic Volatility Models

no code implementations19 Jul 2019 Chen Gong, David S. Stoffer

The model is a nonlinear and non-Gaussian state space model, and consequently is difficult to fit.

Methodology Computation

Are Anchor Points Really Indispensable in Label-Noise Learning?

1 code implementation NeurIPS 2019 Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama

Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points} (i. e., data points that belong to a specific class almost surely).

Learning with noisy labels

Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

1 code implementation14 May 2019 Sheng Wan, Chen Gong, Ping Zhong, Bo Du, Lefei Zhang, Jian Yang

To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.

Classification General Classification +1

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

no code implementations15 Apr 2019 Fanghui Liu, Chen Gong, Xiaolin Huang, Tao Zhou, Jie Yang, DaCheng Tao

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs).

Template Matching Visual Tracking

Ensemble Teaching for Hybrid Label Propagation

no code implementations8 Apr 2019 Chen Gong, DaCheng Tao, Xiaojun Chang, Jian Yang

More importantly, HyDEnT conducts propagation under the guidance of an ensemble of teachers.

A Regularization Approach for Instance-Based Superset Label Learning

no code implementations5 Apr 2019 Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, DaCheng Tao

As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms.

Learning with Inadequate and Incorrect Supervision

no code implementations20 Feb 2019 Chen Gong, Hengmin Zhang, Jian Yang, DaCheng Tao

To address label insufficiency, we use a graph to bridge the data points so that the label information can be propagated from the scarce labeled examples to unlabeled examples along the graph edges.

Image Classification speech-recognition +2

A Survey on Multi-output Learning

no code implementations2 Jan 2019 Donna Xu, Yaxin Shi, Ivor W. Tsang, Yew-Soon Ong, Chen Gong, Xiaobo Shen

Multi-output learning aims to simultaneously predict multiple outputs given an input.

Decision Making

RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology

no code implementations28 Nov 2018 Chen Gong, N. Benjamin Erichson, John P. Kelly, Laura Trutoiu, Brian T. Schowengerdt, Steven L. Brunton, Eric J. Seibel

To the best of our knowledge, this is the first template matching algorithm for retina images with small template images from unconstrained retinal areas.

Dimensionality Reduction Mixed Reality +1

Learning Data-adaptive Nonparametric Kernels

no code implementations31 Aug 2018 Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li

Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility.

Adversarial Metric Learning

no code implementations9 Feb 2018 Shuo Chen, Chen Gong, Jian Yang, Xiang Li, Yang Wei, Jun Li

In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs.

Metric Learning

Multi-Grained Chinese Word Segmentation

no code implementations EMNLP 2017 Chen Gong, Zhenghua Li, Min Zhang, Xinzhou Jiang

Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence.

Chinese Word Segmentation Language Modelling

Indefinite Kernel Logistic Regression with Concave-inexact-convex Procedure

no code implementations6 Jul 2017 Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens

Since the concave-convex procedure has to solve a sub-problem in each iteration, we propose a concave-inexact-convex procedure (CCICP) algorithm with an inexact solving scheme to accelerate the solving process.


Saliency Propagation From Simple to Difficult

no code implementations CVPR 2015 Chen Gong, DaCheng Tao, Wei Liu, Stephen J. Maybank, Meng Fang, Keren Fu, Jie Yang

In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner.

Saliency Detection

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