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118 results on '"Hu, Junlin"'

3. Intra-variance Guided Metric Learning for Face Forgery Detection

Author

Chen, Zhentao, Hu, Junlin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jia, Wei, editor, Kang, Wenxiong, editor, Pan, Zaiyu, editor, Ben, Xianye, editor, Bian, Zhengfu, editor, Yu, Shiqi, editor, He, Zhaofeng, editor, and Wang, Jun, editor

Published
2023
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4. A Comparative Study on Canonical Correlation Analysis-Based Multi-feature Fusion for Palmprint Recognition

Author

Wu, Yihang, Hu, Junlin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jia, Wei, editor, Kang, Wenxiong, editor, Pan, Zaiyu, editor, Ben, Xianye, editor, Bian, Zhengfu, editor, Yu, Shiqi, editor, He, Zhaofeng, editor, and Wang, Jun, editor

Published
2023
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5. Sparsity-Regularized Geometric Mean Metric Learning for Kinship Verification

Author

Xu, Yunhao, Hu, Junlin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Deng, Weihong, editor, Feng, Jianjiang, editor, Huang, Di, editor, Kan, Meina, editor, Sun, Zhenan, editor, Zheng, Fang, editor, Wang, Wenfeng, editor, and He, Zhaofeng, editor

Published
2022
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6. Unsupervised Domain Adaptation via Attention Augmented Mutual Networks for Person Re-identification

Author

Tian, Hui, Hu, Junlin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fang, Lu, editor, Chen, Yiran, editor, Zhai, Guangtao, editor, Wang, Jane, editor, Wang, Ruiping, editor, and Dong, Weisheng, editor

Published
2021
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7. Kinship Verification via Reference List Comparison

Author

Zheng, Wenna, Hu, Junlin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Feng, Jianjiang, editor, Zhang, Junping, editor, Liu, Manhua, editor, and Fang, Yuchun, editor

Published
2021
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11. Virtual reality enhances the social skills of children with autism spectrum disorder: a review.

Author

Chen, Jingying, Hu, Junlin, Zhang, Kun, Zeng, Xiao, Ma, Yuhao, Lu, Wenrui, Zhang, Kai, and Wang, Guangshuai

Subjects
*VIRTUAL reality, *SOCIAL skills in children, *AUTISM spectrum disorders, *AUTISTIC children, *INTERPERSONAL relations in children
Abstract

Virtual reality (VR) technology has shown great potential on intervention of social skills for children with autism spectrum disorder (ASD). This review aimed to review the application of VR in current social skills intervention targeting autistic children and evaluate its effect. After searching and screening three databases, we found 20 papers on interventions for children with ASD using VR technology, including three studies of social communication, six studies about social–emotional reciprocity, nine studies on social interaction, and eight studies of on social cognition. First, concerning the age of the children involved in the interventions, we found that VR devices were more suitable for children with ASD who were older. Second, regarding VR devices, we recommend laptop VR devices based on the cost, non-invasiveness, and effectiveness of their use in interventions. The VR application form of game is more advisable. More importantly, for the design of the VR intervention, based on the adoption article, we found that using a single participant worked better. This research presents the overall effects and limitations of this literature as well as implications for future work. [ABSTRACT FROM AUTHOR]

Published
2024
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12. A Novel Approach for the Analysis of Ship Pollution Accidents Using Knowledge Graph.

Author

Hu, Junlin, Zhou, Weixiang, Zheng, Pengjun, and Liu, Guiyun

Abstract

Ship pollution accidents can cause serious harm to marine ecosystems and economic development. This study proposes a ship pollution accident analysis method based on a knowledge graph to solve the problem that complex accident information is challenging to present clearly. Based on the information of 411 ship pollution accidents along the coast of China, the Word2vec's word vector models, BERT–BiLSTM–CRF model and BiLSTM–CRF model, were applied to extract entities and relations, and the Neo4j graph database was used for knowledge graph data storage and visualization. Furthermore, the case information retrieval and cause correlation of ship pollution accidents were analyzed by a knowledge graph. This method established 3928 valid entities and 5793 valid relationships, and the extraction accuracy of the entities and relationships was 79.45% and 82.47%, respectively. In addition, through visualization and Cypher language queries, we can clearly understand the logical relationship between accidents and causes and quickly retrieve relevant information. Using the centrality algorithm, we can analyze the degree of influence between accident causes and put forward targeted measures based on the relevant causes, which will help improve accident prevention and emergency response capabilities and strengthen marine environmental protection. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Combined Descriptors in Spatial Pyramid Domain for Image Classification

Author

Hu, Junlin and Guo, Ping

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.9, I.5.4
Abstract

Recently spatial pyramid matching (SPM) with scale invariant feature transform (SIFT) descriptor has been successfully used in image classification. Unfortunately, the codebook generation and feature quantization procedures using SIFT feature have the high complexity both in time and space. To address this problem, in this paper, we propose an approach which combines local binary patterns (LBP) and three-patch local binary patterns (TPLBP) in spatial pyramid domain. The proposed method does not need to learn the codebook and feature quantization processing, hence it becomes very efficient. Experiments on two popular benchmark datasets demonstrate that the proposed method always significantly outperforms the very popular SPM based SIFT descriptor method both in time and classification accuracy., Comment: 9 pages, 5 figures

Published
2012

22. Large Margin Multi-metric Learning for Face and Kinship Verification in the Wild

Author

Hu, Junlin, Lu, Jiwen, Yuan, Junsong, Tan, Yap-Peng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cremers, Daniel, editor, Reid, Ian, editor, Saito, Hideo, editor, and Yang, Ming-Hsuan, editor

Published
2015
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25. Adaptively Weighted k-Tuple Metric Network for Kinship Verification

Author

Huang, Sheng, Lin, Jingkai, Huangfu, Luwen, Xing, Yun, Hu, Junlin, and Zeng, Daniel Dajun

Abstract

Facial image-based kinship verification is a rapidly growing field in computer vision and biometrics. The key to determining whether a pair of facial images has a kin relation is to train a model that can enlarge the margin between the faces that have no kin relation while reducing the distance between faces that have a kin relation. Most existing approaches primarily exploit duplet (i.e., two input samples without cross pair) or triplet (i.e., single negative pair for each positive pair with low-order cross pair) information, omitting discriminative features from multiple negative pairs. These approaches suffer from weak generalizability, resulting in unsatisfactory performance. Inspired by human visual systems that incorporate both low-order and high-order cross-pair information from local and global perspectives, we propose to leverage high-order cross-pair features and develop a novel end-to-end deep learning model called the adaptively weighted $k$ -tuple metric network (AW $k$ -TMN). Our main contributions are three-fold. First, a novel cross-pair metric learning loss based on $k$ -tuplet loss is introduced. It naturally captures both the low-order and high-order discriminative features from multiple negative pairs. Second, an adaptively weighted scheme is formulated to better highlight hard negative examples among multiple negative pairs, leading to enhanced performance. Third, the model utilizes multiple levels of convolutional features and jointly optimizes feature and metric learning to further exploit the low-order and high-order representational power. Extensive experimental results on three popular kinship verification datasets demonstrate the effectiveness of our proposed AW $k$ -TMN approach compared with several state-of-the-art approaches. The source codes and models are released.1

Published
2023
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