Zhang Le (张乐)

I am a scientist at Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), where I work on Deep Learning.

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PhD: Nanyang Technological University, Singapore (2012-2016).

MSC: Nanyang Technological University, Singapore (2011-2012).

BEng: University of Electronic Science and Technology of China (2007-2011).

Working Experience

Scientist (09/2018-present) : Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR).

Postdoc (02/2016-09/2018) : Advanced Digital Sciences Center.


01/2019: We organized an worksop on "Deep Learning for Human Activity Recognition" in IJCAI2019. Selected papers (or extensions) could be published on a special issue of "Deep Learning for Human Activity Recognition" at Elsevier Journal, Neurocomputing.

10/2018: We organized an special issue on "Ensemble Deep Learning" in Pattern Recognition.

09/2018: I joined I2R as a scientist.

05/2018: In OMG-Emotion Challenge 2018, our ADSC team's submissions ranked 1st for vision-only arousal/valence prediction and 2nd for overall valence prediction!


I'm interested in machine learning, deep learning, computer vision, image processing and their applications.

Nonlinear Regression Via Deep Negative Correlation Learning
Le Zhang, Zenglin Shi, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Joey Tianyi Zhou, Guoyan Zheng, Zeng Zeng
IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019.
project page / arXiv

We provide a general deep regression framework which mimics ensemble learning with a single model. We demonstrate its effectiveness on several computer vision tasks including corwd counting, age estimation, apparent personality analysis and image super-resolution.

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship
Le Zhang, Songyou Peng and Stefan Winkler.
IEEE Transactions on Affective Computing, 2019.

A journal extension of our ACM MM paper for joint analysis of apparent personality, emotion and their relationship

Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
Jia-Xing Zhao, Yang Cao, Deng-Ping Fan, Ming-Ming Cheng, Xuan-Yi Li, Le Zhang
IEEE Conference on Computer Vision and Pattern Recognition , 2019.

In this paper, we utilize contrast prior, which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-based architecture to enhance the depth information.

An Evaluation of Feature Matchers for Fundamental Matrix Estimation
Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid
British Machine Vision Conference , 2019.

This paper evaluates the recently proposed local features, correspondence pruning algorithms, and robust estimators using strictly defined metrics in the context of image matching and fundamental matrix estimation. Comprehensive evaluation results on four large-scale datasets provide insights into which datasets are particularly challenging and which algorithms perform well in which scenarios.

Heterogeneous Oblique Random Forest
Rakesh Katuwal, P.N. Suganthan and Le Zhang.
Pattern Recognition, 2019.

We propose a heterogeneous oblique random forest that employs an oblique linear hyperplane at each node. On benchmarking 190 classifiers on 121 UCI datasets, we find that the oblique random forests proposed in this paper are the top 3 ranked classifiers with the heterogeneous oblique random forest being statistically significantly better than all other classifiers

Deep Learning based Human Activity Recognition for Healthcare Services
Zhenghua Chen, Le Zhang, Wu min, Xiaoli Li.
in book “Deep Learning for Biomedical Data Analysis: Techniques, Approaches and Applications”, Springer, to be published in 2020.

Light Sensor Based Occupancy Estimation via Bayes Filter with Neural Networks
Zhenghua Chen, Yanbing Yang, Chaoyang Jiang, Jie Hao, Le Zhang
IEEE Transactions on Industrial Electronics , 2019.

A Bayes filter with neural networks is proposed for the optimal estimation of occupancy based on light sensor data.

WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM
Zhenghua Chen, Le Zhang*,Chaoyang Jiang, Zhiguang Cao, and Wei Cui (* indicates the corresponding author)
IEEE Transaction on Mobile Computing , preprint.

An attention based bi-directional long short-term memory for passive human activity recognition using WiFi CSI signals.

Richer Convolutional Features for Edge Detection
Yun Liu, Ming-Ming Cheng, Xiaowei Hu, Jia-Wang Bian, Le Zhang, Xiang Bai and Jinhui Tang
IEEE Transaction on Pattern Analysis and Machine Intelligence , preprint.
project page / Blog

An accurate edge detector using richer convolutional features.

Robust Mobile Location Estimation in NLOS Environment Using GMM, IMM, and EKF
Wei Cui, Bing Li, Le Zhang and Wei Meng,
IEEE Systems Journal , preprint.

A mobile location estimation scheme for realistically mixed LOS/NLOS/LOS-NLOS environments.

Using FTOC to Track Shuttlecock for the Badminton Robot
Wei Chen, Tingbo Liao, Zhihang Li, HaozhiLin, Hong Xue,Le Zhang, Jing Guo, Zhiguang Cao
Neurocomputing , preprint.

An omnidirectional mobile badminton robot, which is composed of mechanical, visual and motion control subsystems.

An ensemble of decision trees with random vector functional link networks for multi-class classification
Katuwal, Rakesh, P. N. Suganthan, and Le Zhang
Applied Soft Computing , 2018.

A new ensemble of classifiers that consists of decision trees and random vector functional link network for multi-class classification

Distilling the Knowledge from Handcrafted Features for Human Activity Recognition
Chen, Zhenghua, Le Zhang*, Zhiguang Cao, and Jing Guo (* indicates the corresponding author)
IEEE Transactions on Industrial Informatics , 2018.

A novel knowledge distilling strategy to improve deep learning with handcrafed features

Multiscale Multitask Deep NetVLAD for Crowd Counting
Zenglin Shi, Le Zhang, Yibo Sun, and Yangdong Ye
IEEE Transactions on Industrial Informatics , 2018.
project page

we introduce a dynamic augmentation technique to train a much deeper CNN for crowd counting. In order to decrease over-fitting caused by limited number of training samples, multitask learning is further employed to learn generalisable representations across similar domains. We also propose to aggregate multi-scale convolutional features extracted from the entire image into a compact single vector representation amenable to efficient and accurate counting by way of "Vector of Locally Aggregated Descriptors" (VLAD).

Received Signal Strength Based Indoor Positioning Using a Random Vector Functional Link Network
Cui, Wei, Le Zhang *,, Bing Li, Jing Guo, Wei Meng, Haixia Wang, and Lihua Xie (* indicates the corresponding author)
IEEE Transactions on Industrial Informatics , 2018.

A robust and parallel RVFL for RSS based indoor positioning.

Historical Context-based Style Classification of Painting Images via Label Distribution Learning
Jufeng Yang, Liyi Chen, Le Zhang , Xiaoxiao Sun, Dongyu She, Shao-Ping Lu and Ming-Ming Cheng
ACM Multimedia , 2018.

Novel knowledge distilling strategy to assist visual feature learning in the convolutional neural network for painting style classification.

Give Me One Portrait Image, I Will Tell You Your Emotion and Personality
Songyou Peng, Le Zhang , Stefan Winkler and Marianne Winslett
ACM Multimedia , 2018.

A technical Demo. A deep Siamese-like network is introduced to predict one's Big-Five personality and arousal-valence emotion from one portrait photo.

Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for WholeHeart Segmentation from 3D MR Images
Zenglin Shi, Guodong Zeng,Le Zhang , Xiahai Zhuang, Lei Li, Guang Yang, and Guoyan Zheng,
International Conference On Medical Image Computing & Computer Assisted Intervention , 2018.

A probabilistic deep voxelwise dilated residual network to segment the whole heart from 3D MR images.

DEL: Deep Embedding Learning for Efficient Image Segmentation
Yun Liu, Peng-Tao Jiang, Xiaowei Hu, Vahan Petrosyan, Shi-Jie Li, Jia-Wang Bian, Le Zhang, and Ming-ming Cheng
International Joint Conference on Artificial Intelligence , 2018.

We train a fully convolutional network to learn the feature embedding space for each superpixel.

Crowd Counting With Deep Negative Correlation Learning
Zenglin Shi, Le Zhang *, Yun Liu, Xiaofeng Cao, Yangdong Ye, Shi-Jie Li, and Guoyan Zheng (* indicates the corresponding author)
IEEE Conference on Computer Vision and Pattern Recognition , 2018.
project page / Blog

With no extra parameters, we mimic ensemble learning within a single network.

Kernel Cross-Correlator
Chen Wang, Le Zhang, Lihua Xie, Junsong Yuan,
AAAI Conference on Artificial Intelligence , 2018.
project page / Blog

KCC extends KCF to any kernel function and is not limited to circulant structure on training data, thus it is able to predict affine transformations with customized properties.

Visual Tracking With Convolutional Random Vector Functional Link Network
Le Zhang and Ponnuthurai Nagaratnam Suganthan,
IEEE Transaction on Cybernetics, 2017.
project page

Ensemble of randomized ConvNets for visual tracking.

Robust visual tracking via co-trained Kernelized correlation filters
Le Zhang and Ponnuthurai Nagaratnam Suganthan,
Pattern Recognition, 2017.
project page

Ensemble of KCFs for visual tracking.

Benchmarking Ensemble Classifiers with Novel Co-Trained Kernel Ridge Regression and Random Vector Functional Link Ensembles
Le Zhang, and Ponnuthurai Nagaratnam Suganthan,
IEEE Computational Intelligence Magazine , 2017.

A benchmark summarization for my PhD study.

Robust Human Activity Recognition Using Smartphone Sensors via CT-PCA and Online SVM
Zhenghua Chen, Qingchang Zhu, Yeng Chai Soh and Le Zhang* (* indicates the corresponding author)
IEEE Transaction on Industrial Informatics , 2017.

An online SVM for time series signal.

Oblique random forest ensemble via Least Square Estimation for time series forecasting
Xueheng Qiu, Le Zhang, Ponnuthurai Nagaratnam Suganthan, and Gehan A.J. Amaratunga,
Information Sciences , 2017.

Extend the oblique random forest for regression problems.

Finding the ‘faster’ path in vehicle routing
Guo, Jing, Yaoxin Wu, Xuexi Zhang, Le Zhang, Wei Chen, Zhiguang Cao, Lu Zhang, and Hongliang Guo
IET Intelligent Transport Systems , 2017.

Improve the faster criterion in vehicle routing by extending the bi-delta distribution to the binormal distribution.

Robust Visual Tracking Using Oblique Random Forests
Le Zhang, Jagan Varadarajan, Ponnuthurai Nagaratnam Suganthan, Narendra Ahuja, and Pierre Moulin,
IEEE Conference on Computer Vision and Pattern Recognition , 2017.
project page

An incremental oblique random forest.

Robust Multi-Modal Cues for Dyadic Human Interaction Recognition
Trabelsi, Rim, Jagannadan Varadarajan, Yong Pei, Le Zhang, Issam Jabri, Ammar Bouallegue, and Pierre Moulin.
ACM Multimedia Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes , 2017.

We addressed the problem of dyadic interaction recognition using multi-modal data by combining all three modalities and uses both person centric features (via proxemic descriptors from 3D joints) and holistic features (via FCNN based color and depth features).

Ensemble classification and regression-recent developments, applications and future directions
Le Zhang*, Ye Ren* and Ponnuthurai Nagaratnam Suganthan (* indicates co-first authors).
IEEE Computational Intelligence Magazine, 2016.

This paper reviews traditional emsemble learning as well as state-of-the-art deep ensemble methods and thus can serve as an extensive summary for practitioners and beginner.

A Comprehensive Evaluation of Random Vector Functional Link Networks
Le Zhang, and Ponnuthurai Nagaratnam Suganthan .
Information Sciences, 2016.

Benchmark evaluation of RVFL.

A Survey of Randomized Algorithms for Training Neural Networks
Le Zhang, and Ponnuthurai Nagaratnam Suganthan .
Information Sciences, 2016.

Oblique decision tree ensemble via multisurface proximal support vector machine
Le Zhang, and Ponnuthurai Nagaratnam Suganthan .
IEEE Transaction on Cybernetics, 2015.

Oblique Random Forest by fast iterative clustering.

Visual tracking with convolutional neural network
Le Zhang, and Ponnuthurai Nagaratnam Suganthan .
IEEE International Conference on Systems, Man, and Cybernetics , 2015.

ConvNet for visual tracking.

Random forests with ensemble of feature spaces
Le Zhang, and Ponnuthurai Nagaratnam Suganthan .
Pattern Recognition, 2014.


EE2073-Introduction to EEE Design and Project

Professional Services

Guest Editor: Pattern Recognition Special Issue on Ensemble Deep Learning.

Journal Reviewer: IEEE TPAMI, IEEE TIP, IEEE TCyb, IEEE TNNLS, IEEE TEVC, IEEE TCSVT, IEEE TII, IEEE TPDS, IET Computer Vision, Pattern Recognition, Applied Soft Computing, Expert Systems with Applications.


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