Weiwei Guo

Tongji University

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Zenghui Zhang

Shanghai Jiao Tong University

Wenxian Yu

Shanghai Jiao Tong University

Tao Zhang

Shanghai Jiao Tong University

Mona Diab

Carnegie Mellon University

Jiyuan Tan

North China University of Technology

Huiji Gao

Bay Area Air Quality Management District

Feng Liu

Nanjing University of Posts and Telecommunications

Sida Wang

University of Alberta

Feng Liu

OriginWater (China)

Bin Liu

Ministry of Natural Resources

Cooperative Institutions

Shanghai Jiao Tong University

Chinese Academy of Sciences

Tsinghua University

Tongji University

University of Chinese Academy of Sciences

Chinese PLA General Hospital

North China University of Technology

Ministry of Education of the People's Republic of China

Beijing Institute of Technology

Zhejiang University

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Discriminative 3D human pose estimation from monocular images via topological preserving hierarchical affinity clustering

Weiwei Guo Ioannis Patras

This paper presents a hierarchical approach to address the problem of 3D human body pose estimation from a single images. In order to deal with multimodality, we learn piecewise mappings from observations to human poses. We first construct a tree on the pose manifold by applying affinity propagation clustering at the different levels of the hierarchy. Support vector machines classifiers are then trained to learn traversing the tree so that new examples/ observations can be classified to the clusters associated to the leaf nodes. Multi-valued Relevance Vector Machine (RVM) regressors are trained at each of the leaf nodes, so to learn local mappings from the observation to the pose space. We propose the use of a geodesic distance during clustering and describe a method for training multi-valued RVMs. The latter alleviates the need to train a separate RVM for each of the dimensions in the pose space. We validate the proposed method using the HumanEva dataset and show promising results.

Discriminative model

Hierarchical clustering

Tree (set theory)

Affinity propagation

Traverse

10.1109/iccvw.2009.5457725

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Citations (7)

OpenSARShip 2.0: A large-volume dataset for deeper interpretation of ship targets in Sentinel-1 imagery

Boying Li Bin Liu Lanqing Huang Weiwei Guo Zenghui Zhang

In the era of big data for synthetic aperture radar (SAR), multiple SAR systems have come into service, including Sentinel-1. The support of large-volume datasets is the key to the deep interpretation of ship targets in Sentinel-1 imagery. In this paper, we present OpenSARShip 2.0, the newest and upgraded version of OpenSARShip, which is dedicated to the deeper interpretation of SAR imagery for marine surveillance. Based on the higher requirements for ship target interpretation, the OpenSARShip 2.0, covering 34528 SAR ship chips with automatic identification system (AIS) information, possesses three improvements: large volume, interference labeling, and type levels, which are described in this paper. In addition, an application case involving the geometric parameter extraction is introduced to demonstrate the applicability of the large dataset.

Identification

10.1109/bigsardata.2017.8124929

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Citations (50)

Collaborative Training and Mechanism Optimization for Legal Professionals in the Context of “Double Carbon” Initiatives

Advance in Law (2025)

Weiwei Guo

10.35534/al.0701010

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Citations (0)

A domain specific knowledge extraction transformer method for multisource satellite-borne SAR images ship detection

ISPRS Journal of Photogrammetry and Remote Sensing (2023)

Siyuan Zhao Ying Luo Tao Zhang Weiwei Guo Zenghui Zhang

Multisource satellite-borne synthetic aperture radar (SAR) images have different probability distributions. Traditional supervised learning, consequently, cannot achieve good test performance on one novel satellite-borne SAR dataset while training a good model on another existing satellite-borne SAR dataset. In this article, a domain adaptation (DA) Transformer object detection method is proposed to solve the unlabeled multisource satellite-borne SAR image object detection problem. Unlike existing DA methods based on convolutional neural network (CNN) that focus more on multi-level local feature extraction, we choose to use Vision Transformer (ViT) Faster region CNN (FRCNN) as the baseline network to cope with the extraction of global features of SAR images. Then, two classification tokens are used to learn the mapping of different domains and fully extract domain-specific knowledge, generating two different feature spaces that rely on the original label and pseudo-label to train the source and target domains feature spaces, respectively. Besides, the pseudo-label of target domain is also refined and reconstructed by feature clustering in order to improve the accuracy of target domain knowledge. Finally, the original detection head of FRCNN is employed to detect the target domain SAR image objects. Extensive experiments on image datasets from multisource satellite-borne SAR such as Gaofen-3, TerraSAR-X, Sentinel-1, and RadarSAT-2 show that compared to the other state of the art (SOTA) methods, the proposed method can achieve the greatest object detection accuracy. Especially, taking the recently proposed Transformer-based method as an example, our method has more than 5% improvement in accuracy and more than 16% reduction in training time.

10.1016/j.isprsjprs.2023.02.011

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Citations (28)

Polyetheretherketone biomaterials and their current progress, modification-based biomedical applications and future challenges

Materials & Design (2025)

Zhanli Yang Weiwei Guo Wenhao Yang Jianye Song Wenhui Hu

Surface Modification

10.1016/j.matdes.2025.113716

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Citations (0)

Designing a Smart Shirt to Support Adolescents’ Sitting Posture Based on Strain Textile Sensors

Lecture notes in computer science (2021)

Qi Wang Xin Zhou Weiwei Guo Xiaohua Sun

Sitting

Textile

Strain (injury)

10.1007/978-3-030-90966-6_12

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Citations (1)

Quality prediction of industrial polyethylene process with robust long short term memory model

Qiao Liu Weiwei Guo Liangfeng Xu Zengliang Gao Yi Liu

A challenge for construction of accurate soft sensors in the process industries is that industrial process data often contains various noise and outliers. A robust long short term memory (LSTM) neural network with the maximum correntropy criterion (MCC) is proposed to build a reliable soft sensor model. The proposed model employs an MCC-based objective function centred on a Gaussian kernel. Without tedious preprocessing approaches for process data, the proposed model can assign smaller weights to outliers to reduce their negative effects on prediction. Consequently, it can achieve better prediction performance compared with the traditional LSTM soft sensor without robust strategy. The quality prediction results on an industrial polyethylene process demonstrate its effectiveness and advantages.

Soft sensor

Kernel (algebra)

Data pre-processing

10.1109/docs55193.2022.9967703

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Citations (1)

FitSleeve: Designing Wearable Display and Feedback to Improve the Fitness Experience and Motivation

[ ] With Design: Reinventing Design Modes (2022)

Qi Wang Zhiyu Li Weiwei Guo Xiaohua Sun

In this article, we focus on fitness workout scenarios. To examine the current practices, we first conducted a substantial user research, through the content analysis procedure we summarized insights regarding fitness purpose, essential data, psychological state, emotional changes and social habits. Subsequently, we generalized related design opportunities of improving the fitness experience and motivation to enhance the fitness performance and communication. Feedback and data representation have great potential to address the challenges, therefore we first proposed a design process for the feedback mode design of fitness workout, including the contextual information, feedback strategy, and realization ways. The feedback designs are implemented on a wearable augmented feedback system 'FitSleeve', focusing on individual and group scenarios separately. In group sessions, FitSleeve can demonstrate participants' experience level, real-time heart rate zone and feedback regarding the correct movement execution. In individual sessions, FitSleeve can display the training progress and provide continuous encouragement. Finally, we evaluated FitSleeve by adopting the System Usability Scale, User Experience Questionnaire, Intrinsic Motivation Inventory and user subjective interviews. The findings indicate the potential of FitSleeve to improve the user experience and motivation of fitness participants.

Realization (probability)

10.1007/978-981-19-4472-7_198

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Citations (0)

Change in Heart Rate Variability Indexes due to high driving workload in turning left at the intersection in real road environment

Weiwei Guo Xiaoting Tian Jiyuan Tan Li Wang

The paper intends to investigate the changes of Heart Rate Variability (HRV) indexes of healthy drivers while driving on a real urban road environment in order to excavate the variation of drivers' driving workload when turning left.Electrocardiograph (ECG) was recorded continuously while drivers driving on a familiar road.The data from three drives were collected for analyzing every fifty minutes.We mainly analyzed the RMSSD, PNN50 and LF/HF of HRV in time-frequency domain which reflects the activity of autonomic nerve and sympathetic nerve.The HRV was extracted from the ECG signal de-noised and filtered.The results show that RMSSD, PNN50 and LF/HF of the driver obviously increased when turning left on the urban road.This reflects that drivers' HRV will be changed significantly with the heavier visual workload.Our findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring.

Driving simulation

10.2991/icmemtc-16.2016.158

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Citations (4)

Cascaded Deep Neural Ranking Models in LinkedIn People Search

Zimeng Yang Song Yan Abhimanyu Lad Xiaowei Liu Weiwei Guo

LinkedIn connects the world's professionals to make them more productive and successful. People Search plays an important role in fulfilling this goal by helping members find the most relevant and personalized results through a broad range of queries like names, job titles, skills, companies, locations, etc. It is one of the biggest search verticals at LinkedIn both in terms of engineering footprint and search traffic. In this paper, we present an overview of the People Search system, and discuss how we build and serve deep neural network (DNN) models, leveraging state-of-the-art deep natural language processing (NLP) techniques (e.g., convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT)). We describe our journey of applying deep neural ranking models to a real-life product, including the modeling and system bottleneck challenges, crucial design choices, and lessons learned along the way. We hope a story of our endeavors and successes will provide meaningful insights to other similar systems.

Deep Neural Networks

10.1145/3459637.3481899

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Citations (4)