Search Results for author: Qiang Wu

Found 53 papers, 13 papers with code

Cooperative Network Learning for Large-Scale and Decentralized Graphs

1 code implementation3 Nov 2023 Qiang Wu, Yiming Huang, Yujie Zeng, Yijie Teng, Fang Zhou, Linyuan Lü

Here, we introduce a Cooperative Network Learning (CNL) framework to ensure secure graph computing for various graph tasks.

Graph Learning Link Prediction +2

PB-LLM: Partially Binarized Large Language Models

2 code implementations29 Sep 2023 Yuzhang Shang, Zhihang Yuan, Qiang Wu, Zhen Dong

This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression.

Binarization Quantization

Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

no code implementations22 Sep 2023 Yiming Huang, Yujie Zeng, Qiang Wu, Linyuan Lü

Despite the recent successes of vanilla Graph Neural Networks (GNNs) on many tasks, their foundation on pairwise interaction networks inherently limits their capacity to discern latent higher-order interactions in complex systems.

Node Classification Node Property Prediction

Masked Cross-image Encoding for Few-shot Segmentation

no code implementations22 Aug 2023 Wenbo Xu, Huaxi Huang, Ming Cheng, Litao Yu, Qiang Wu, Jian Zhang

Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images.

Few-Shot Semantic Segmentation

Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction

no code implementations7 Aug 2023 Zhixiong Di, Runzhe Tao, Lin Chen, Qiang Wu, Yibo Lin

With imbalanced distribution of packed and unpacked logic elements, we further propose techniques such as graph oversampling and mini-batch training for this imbalanced learning task in large circuit graphs.

Graph Learning Representation Learning

Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning

no code implementations11 Jul 2023 Rixin Wu, Ran Wang, Jie Hao, Qiang Wu, Ping Wang

Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management.

Management reinforcement-learning

Influential Simplices Mining via Simplicial Convolutional Network

no code implementations11 Jul 2023 Yujie Zeng, Yiming Huang, Qiang Wu, Linyuan Lü

It can tackle higher-order tasks by leveraging novel higher-order presentations: hierarchical bipartite graphs and higher-order hierarchical (HoH) Laplacians, where targeted simplices are grouped into a hub set and can interact with other simplices.

Graph Learning

RPTQ: Reorder-based Post-training Quantization for Large Language Models

1 code implementation3 Apr 2023 Zhihang Yuan, Lin Niu, Jiawei Liu, Wenyu Liu, Xinggang Wang, Yuzhang Shang, Guangyu Sun, Qiang Wu, Jiaxiang Wu, Bingzhe Wu

In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers.


Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance

no code implementations23 Mar 2023 Zhihang Yuan, Jiawei Liu, Jiaxiang Wu, Dawei Yang, Qiang Wu, Guangyu Sun, Wenyu Liu, Xinggang Wang, Bingzhe Wu

Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures.

Benchmarking Data Augmentation +1

DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL

no code implementations2 Nov 2022 Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Bo Du, Akbar Telikani, Jianqing Wu, Shubin Xie

Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC).


Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding

1 code implementation6 Oct 2022 Guofeng Mei, Cristiano Saltori, Fabio Poiesi, Jian Zhang, Elisa Ricci, Nicu Sebe, Qiang Wu

Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.

3D Object Classification Contrastive Learning +3

Lightweight Vision Transformer with Cross Feature Attention

no code implementations15 Jul 2022 Youpeng Zhao, Huadong Tang, Yingying Jiang, Yong A, Qiang Wu

Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks.

Inductive Bias object-detection +2

Self-attention on Multi-Shifted Windows for Scene Segmentation

1 code implementation10 Jul 2022 Litao Yu, Zhibin Li, Jian Zhang, Qiang Wu

Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label.

Descriptive Scene Segmentation +1

Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting

no code implementations5 Feb 2022 Guofeng Mei, Litao Yu, Qiang Wu, Jian Zhang, Mohammed Bennamoun

This paper proposes a general unsupervised approach, named \textbf{ConClu}, to perform the learning of point-wise and global features by jointly leveraging point-level clustering and instance-level contrasting.

3D Object Classification Clustering +2

Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

1 code implementation19 Dec 2021 Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu

Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem.

Reinforcement Learning (RL)

Efficient Pressure: Improving efficiency for signalized intersections

1 code implementation4 Dec 2021 Qiang Wu, Liang Zhang, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu

Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem.

Reinforcement Learning (RL)

PTQ4ViT: Post-Training Quantization Framework for Vision Transformers with Twin Uniform Quantization

1 code implementation24 Nov 2021 Zhihang Yuan, Chenhao Xue, Yiqi Chen, Qiang Wu, Guangyu Sun

We observe the distributions of activation values after softmax and GELU functions are quite different from the Gaussian distribution.


TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data

no code implementations3 Jun 2021 Pengda Qin, Yuhong Li, Kefeng Deng, Qiang Wu

Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly.

Contrastive Learning Image Classification +1

Clothing Status Awareness for Long-Term Person Re-Identification

no code implementations ICCV 2021 Yan Huang, Qiang Wu, Jingsong Xu, Yi Zhong, Zhaoxiang Zhang

This work argues that these approaches in fact are not aware of clothing status (i. e., change or no-change) of a pedestrian.

Person Re-Identification

PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning

no code implementations20 Dec 2020 Huaxi Huang, Junjie Zhang, Jian Zhang, Qiang Wu, Chang Xu

Second, the extra unlabeled samples are employed to transfer the knowledge from base classes to novel classes through contrastive learning.

Contrastive Learning Few-Shot Learning

Field-wise Learning for Multi-field Categorical Data

1 code implementation NeurIPS 2020 Zhibin Li, Jian Zhang, Yongshun Gong, Yazhou Yao, Qiang Wu

We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters.

Multi-layer Feature Aggregation for Deep Scene Parsing Models

no code implementations4 Nov 2020 Litao Yu, Yongsheng Gao, Jun Zhou, Jian Zhang, Qiang Wu

The proposed module can auto-select the intermediate visual features to correlate the spatial and semantic information.

Scene Parsing Semantic Segmentation

Dual Attention on Pyramid Feature Maps for Image Captioning

no code implementations2 Nov 2020 Litao Yu, Jian Zhang, Qiang Wu

In this paper, we propose to apply dual attention on pyramid image feature maps to fully explore the visual-semantic correlations and improve the quality of generated sentences.

Descriptive Image Captioning

A Framework of Learning Through Empirical Gain Maximization

no code implementations29 Sep 2020 Yunlong Feng, Qiang Wu

Furthermore, it is shown that several well-known robust nonconvex regression paradigms, such as Tukey regression and truncated least square regression, can be reformulated into this new framework.

Learning Theory regression

A Statistical Learning Assessment of Huber Regression

no code implementations27 Sep 2020 Yunlong Feng, Qiang Wu

Third, with an adaptive choice of the scale parameter, we demonstrate that Huber regression estimators can be asymptotic mean regression calibrated under $(1+\epsilon)$-moment conditions ($\epsilon>0$).


Optimal Rates of Distributed Regression with Imperfect Kernels

no code implementations30 Jun 2020 Hongwei Sun, Qiang Wu

Then we perform a leave one out analysis of the kernel ridge regression and bias corrected kernel ridge regression, which in combination with the aforementioned framework allows us to derive sharp error bounds and capacity independent optimal rates for the associated distributed kernel regression algorithms.


TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples

no code implementations28 May 2020 Huaxi Huang, Jun-Jie Zhang, Jian Zhang, Qiang Wu, Chang Xu

The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i. e.,} Fine-Grained categorization problems under the Few-Shot setting (FGFS).

Fine-Grained Visual Categorization

SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification

no code implementations ICCV 2019 Yan Huang, Qiang Wu, JingSong Xu, Yi Zhong

We observe that if backgrounds in the training and testing datasets are very different, it dramatically introduces difficulties to extract robust pedestrian features, and thus compromises the cross-domain person re-ID performance.

Person Re-Identification

Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification

no code implementations4 Aug 2019 Huaxi Huang, Jun-Jie Zhang, Jian Zhang, Jingsong Xu, Qiang Wu

A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric.

Classification Few-Shot Learning +2

Model Adaptation via Model Interpolation and Boosting for Web Search Ranking

no code implementations22 Jul 2019 Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Svore, Yi Su, Nazan Khan, Shalin Shah, Hongyan Zhou

This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm.


Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points

no code implementations2 Jul 2019 Zhibin Li, Jian Zhang, Qiang Wu, Yongshun Gong, Jin-Feng Yi, Christina Kirsch

In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels.

Online Learning for Supervised Dimension Reduction

no code implementations ICLR 2019 Ning Zhang, Qiang Wu

The purpose of this paper is to propose new online learning approaches for supervised dimension reduction.

Dimensionality Reduction regression

Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning

1 code implementation7 Apr 2019 Huaxi Huang, Jun-Jie Zhang, Jian Zhang, Qiang Wu, Jingsong Xu

Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric.

General Classification Meta-Learning

Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement

no code implementations11 Mar 2019 Xiaoshui Huang, Lixin Fan, Qiang Wu, Jian Zhang, Chun Yuan

Accurate and fast registration of cross-source 3D point clouds from different sensors is an emerged research problem in computer vision.

Point Cloud Registration

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Overlapping Sliced Inverse Regression for Dimension Reduction

no code implementations23 Jun 2018 Ning Zhang, Zhou Yu, Qiang Wu

The new algorithm, called overlapping sliced inverse regression (OSIR), is able to estimate the effective dimension reduction space and determine the number of effective factors more accurately.

Dimensionality Reduction regression

Multi-pseudo Regularized Label for Generated Data in Person Re-Identification

no code implementations21 Jan 2018 Yan Huang, Jinsong Xu, Qiang Wu, Zhedong Zheng, Zhao-Xiang Zhang, Jian Zhang

Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0, 1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data.

Person Re-Identification Pseudo Label

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network

no code implementations7 Aug 2017 Zheng-Chu Guo, Lei Shi, Qiang Wu

Regularization kernel network is an effective and widely used method for nonlinear regression analysis.

Learning Theory regression

Robust Guided Image Filtering

no code implementations28 Mar 2017 Wei Liu, Xiaogang Chen, Chunhua Shen, Jingyi Yu, Qiang Wu, Jie Yang

In this paper, we propose a general framework for Robust Guided Image Filtering (RGIF), which contains a data term and a smoothness term, to solve the two issues mentioned above.

A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds

no code implementations24 Oct 2016 Xiaoshui Huang, Jian Zhang, Qiang Wu, Lixin Fan, Chun Yuan

In this paper, different from previous ICP-based methods, and from a statistic view, we propose a effective coarse-to-fine algorithm to detect and register a small scale SFM point cloud in a large scale Lidar point cloud.

Bias Correction for Regularized Regression and its Application in Learning with Streaming Data

no code implementations15 Mar 2016 Qiang Wu

We propose an approach to reduce the bias of ridge regression and regularization kernel network.

Incremental Learning regression

Data Driven Robust Image Guided Depth Map Restoration

no code implementations26 Dec 2015 Wei Liu, Yun Gu, Chunhua Shen, Xiaogang Chen, Qiang Wu, Jie Yang

Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight (ToF) are usually contaminated by missing data, noises and suffer from being of low resolution.

LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

no code implementations8 Nov 2015 Steven C. H. Hoi, Xiongwei Wu, Hantang Liu, Yue Wu, Huiqiong Wang, Hui Xue, Qiang Wu

In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images.

Logo Recognition object-detection +1

Robust High Quality Image Guided Depth Upsampling

no code implementations17 Jun 2015 Wei Liu, Yijun Li, Xiaogang Chen, Jie Yang, Qiang Wu, Jingyi Yu

A popular solution is upsampling the obtained noisy low resolution depth map with the guidance of the companion high resolution color image.

Vocal Bursts Intensity Prediction

A new approach for physiological time series

no code implementations23 Apr 2015 Dong Mao, Yang Wang, Qiang Wu

We developed a new approach for the analysis of physiological time series.

Time Series Time Series Analysis

Multiple Authors Detection: A Quantitative Analysis of Dream of the Red Chamber

1 code implementation19 Dec 2014 Xianfeng Hu, Yang Wang, Qiang Wu

Inspired by the authorship controversy of Dream of the Red Chamber and the application of machine learning in the study of literary stylometry, we develop a rigorous new method for the mathematical analysis of authorship by testing for a so-called chrono-divide in writing styles.

Consistency Analysis of an Empirical Minimum Error Entropy Algorithm

no code implementations17 Dec 2014 Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou

The error entropy consistency, which requires the error entropy of the learned function to approximate the minimum error entropy, is shown to be always true if the bandwidth parameter tends to 0 at an appropriate rate.


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