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Journal articles on the topic 'Cross-view Learning'

To see the other types of publications on this topic, follow the link: Cross-view Learning.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Nie, Weizhi, Anan Liu, Wenhui Li, and Yuting Su. "Cross-view action recognition by cross-domain learning." Image and Vision Computing 55 (November 2016): 109–18. http://dx.doi.org/10.1016/j.imavis.2016.04.011.

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2

Wang, Xiuhui, and Wei Qi Yan. "Cross-view gait recognition through ensemble learning." Neural Computing and Applications 32, no. 11 (May 16, 2019): 7275–87. http://dx.doi.org/10.1007/s00521-019-04256-z.

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3

Zhang, Chengkun, Huicheng Zheng, and Jianhuang Lai. "Cross-View Action Recognition Based on Hierarchical View-Shared Dictionary Learning." IEEE Access 6 (2018): 16855–68. http://dx.doi.org/10.1109/access.2018.2815611.

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4

Zhang, Yuqi, Yongzhen Huang, Shiqi Yu, and Liang Wang. "Cross-View Gait Recognition by Discriminative Feature Learning." IEEE Transactions on Image Processing 29 (2020): 1001–15. http://dx.doi.org/10.1109/tip.2019.2926208.

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5

Zheng, Jingjing, Zhuolin Jiang, and Rama Chellappa. "Cross-View Action Recognition via Transferable Dictionary Learning." IEEE Transactions on Image Processing 25, no. 6 (June 2016): 2542–56. http://dx.doi.org/10.1109/tip.2016.2548242.

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6

ZALL, R., and M. R. KEYVANPOUR. "Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation." Advances in Electrical and Computer Engineering 16, no. 2 (2016): 111–24. http://dx.doi.org/10.4316/aece.2016.02015.

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7

Ding, Zhengming, and Yun Fu. "Dual Low-Rank Decompositions for Robust Cross-View Learning." IEEE Transactions on Image Processing 28, no. 1 (January 2019): 194–204. http://dx.doi.org/10.1109/tip.2018.2865885.

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8

Borgia, Alessandro, Yang Hua, Elyor Kodirov, and Neil M. Robertson. "Cross-View Discriminative Feature Learning for Person Re-Identification." IEEE Transactions on Image Processing 27, no. 11 (November 2018): 5338–49. http://dx.doi.org/10.1109/tip.2018.2851098.

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9

Dai, Ju, Ying Zhang, Huchuan Lu, and Hongyu Wang. "Cross-view semantic projection learning for person re-identification." Pattern Recognition 75 (March 2018): 63–76. http://dx.doi.org/10.1016/j.patcog.2017.04.022.

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10

CHEN, Xue, Chunheng WANG, Baihua XIAO, and Song GAO. "Learning Convolutional Domain-Robust Representations for Cross-View Face Recognition." IEICE Transactions on Information and Systems E97.D, no. 12 (2014): 3239–43. http://dx.doi.org/10.1587/transinf.2014edl8095.

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11

Li, Sheng, Ming Shao, and Yun Fu. "Person Re-Identification by Cross-View Multi-Level Dictionary Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 12 (December 1, 2018): 2963–77. http://dx.doi.org/10.1109/tpami.2017.2764893.

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12

Xu, Wanjiang, Canyan Zhu, and Ziou Wang. "Multiview max-margin subspace learning for cross-view gait recognition." Pattern Recognition Letters 107 (May 2018): 75–82. http://dx.doi.org/10.1016/j.patrec.2017.10.033.

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13

Tang, Chang, Xinzhong Zhu, Xinwang Liu, and Lizhe Wang. "Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5101–8. http://dx.doi.org/10.1609/aaai.v33i01.33015101.

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Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2,1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.

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14

Shi, Yujiao, Xin Yu, Liu Liu, Tong Zhang, and Hongdong Li. "Optimal Feature Transport for Cross-View Image Geo-Localization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11990–97. http://dx.doi.org/10.1609/aaai.v34i07.6875.

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This paper addresses the problem of cross-view image geo-localization, where the geographic location of a ground-level street-view query image is estimated by matching it against a large scale aerial map (e.g., a high-resolution satellite image). State-of-the-art deep-learning based methods tackle this problem as deep metric learning which aims to learn global feature representations of the scene seen by the two different views. Despite promising results are obtained by such deep metric learning methods, they, however, fail to exploit a crucial cue relevant for localization, namely, the spatial layout of local features. Moreover, little attention is paid to the obvious domain gap (between aerial view and ground view) in the context of cross-view localization. This paper proposes a novel Cross-View Feature Transport (CVFT) technique to explicitly establish cross-view domain transfer that facilitates feature alignment between ground and aerial images. Specifically, we implement the CVFT as network layers, which transports features from one domain to the other, leading to more meaningful feature similarity comparison. Our model is differentiable and can be learned end-to-end. Experiments on large-scale datasets have demonstrated that our method has remarkably boosted the state-of-the-art cross-view localization performance, e.g., on the CVUSA dataset, with significant improvements for top-1 recall from 40.79% to 61.43%, and for top-10 from 76.36% to 90.49%. We expect the key insight of the paper (i.e., explicitly handling domain difference via domain transport) will prove to be useful for other similar problems in computer vision as well.

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15

Liu, Yixiu, Yunzhou Zhang, Bir Bhanu, Sonya Coleman, and Dermot Kerr. "Multi-level cross-view consistent feature learning for person re-identification." Neurocomputing 435 (May 2021): 1–14. http://dx.doi.org/10.1016/j.neucom.2021.01.010.

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16

Qi, Y. J., Y. P. Kong, and Q. Zhang. "A Cross-View Gait Recognition Method Using Two-Way Similarity Learning." Mathematical Problems in Engineering 2022 (May 23, 2022): 1–14. http://dx.doi.org/10.1155/2022/2674425.

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Gait recognition is a powerful tool for long-distance identification. However, gaits are influenced by walking environments and appearance changes. Therefore, the gait recognition rate declines sharply when the viewing angle changes. In this work, we propose a novel cross-view gait recognition method with two-way similarity learning. Focusing on the relationships between gait elements in three-dimensional space and the wholeness of human body movements, we design a three-dimensional gait constraint model that is robust to view changes based on joint motion constraint relationships. Different from the classic three-dimensional model, the proposed model characterizes motion constraints and action constraints between joints based on time and space dimensions. Next, we propose an end-to-end two-way gait network using long short-term memory and residual network 50 to extract the temporal and spatial difference features, respectively, of model pairs. The two types of difference features are merged at a high level in the network, and similarity values are obtained through the softmax layer. Our method is evaluated based on the challenging CASIA-B data set in terms of cross-view gait recognition. The experimental results show that the method achieves a higher recognition rate than the previously developed model-based methods. The recognition rate reaches 72.8%, and the viewing angle changes from 36° to 144° for normal walking. Finally, the new method also performs better in cases with large cross-view angles, illustrating that our model is robust to viewing angle changes and that the proposed network offers considerable potential in practical application scenarios.

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17

Xia, Zimin, Olaf Booij, Marco Manfredi, and Julian F. P. Kooij. "Cross-View Matching for Vehicle Localization by Learning Geographically Local Representations." IEEE Robotics and Automation Letters 6, no. 3 (July 2021): 5921–28. http://dx.doi.org/10.1109/lra.2021.3088076.

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18

Ning, Zhenyuan, Chao Tu, Xiaohui Di, Qianjin Feng, and Yu Zhang. "Deep cross-view co-regularized representation learning for glioma subtype identification." Medical Image Analysis 73 (October 2021): 102160. http://dx.doi.org/10.1016/j.media.2021.102160.

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19

Deng, Siyang, Wei Xia, Quanxue Gao, and Xinbo Gao. "Cross-view classification by joint adversarial learning and class-specificity distribution." Pattern Recognition 110 (February 2021): 107633. http://dx.doi.org/10.1016/j.patcog.2020.107633.

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20

Hajmohammadi, Mohammad Sadegh, Roliana Ibrahim, and Ali Selamat. "Bi-view semi-supervised active learning for cross-lingual sentiment classification." Information Processing & Management 50, no. 5 (September 2014): 718–32. http://dx.doi.org/10.1016/j.ipm.2014.03.005.

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21

Li, Ao, Yu Ding, Xunjiang Zheng, Deyun Chen, Guanglu Sun, and Kezheng Lin. "Bio-Inspired Structure Representation Based Cross-View Discriminative Subspace Learning via Simultaneous Local and Global Alignment." Complexity 2020 (November 4, 2020): 1–14. http://dx.doi.org/10.1155/2020/8872348.

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Recently, cross-view feature learning has been a hot topic in machine learning due to the wide applications of multiview data. Nevertheless, the distribution discrepancy between cross-views leads to the fact that instances of the different views from same class are farther than those within the same view but from different classes. To address this problem, in this paper, we develop a novel cross-view discriminative feature subspace learning method inspired by layered visual perception from human. Firstly, the proposed method utilizes a separable low-rank self-representation model to disentangle the class and view structure layers, respectively. Secondly, a local alignment is constructed with two designed graphs to guide the subspace decomposition in a pairwise way. Finally, the global discriminative constraint on distribution center in each view is designed for further alignment improvement. Extensive cross-view classification experiments on several public datasets prove that our proposed method is more effective than other existing feature learning methods.

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22

Chen, Xiaoyun, Yeyuan Kang, and Zhiping Chen. "Multi-nonlinear multi-view locality-preserving projection with similarity learning for random cross-view gait recognition." Multimedia Systems 26, no. 6 (September 14, 2020): 727–44. http://dx.doi.org/10.1007/s00530-020-00685-2.

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23

Liu, Long, Le Yang, and Jie Ding. "Transfer Learning and Identification Method of Cross-View Target Trajectory Utilizing HMM." Mathematical Problems in Engineering 2020 (December 24, 2020): 1–13. http://dx.doi.org/10.1155/2020/6656222.

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The behavior identification of the target trajectory is one of the important issues in space behavior analysis. Since the target trajectory model obtained from a fixed view cannot be adapted to the change of the observation perspective, it needs to be retrained when being faced with a new view, which leads to a great amount of increment in application cost. This study proposes a hidden Markov model (HMM) based on the cross-view transfer learning and the recognition method that firstly constructs a linear mapping relationship between the observation matrices of the source and target view utilizing the domain trajectory of the HMMs and obtains the observation matrix parameters of the target domain through the mapping system. Secondly, the transfer probability of the source domain is further optimized to obtain the target domain of the HMM and to identify the behavior of the target domain trajectory utilizing a small number of samples from the view of the target domain. The experimental results denote that the proposed method could effectively realize the identification of the trajectory behavior utilizing a small sample size in the target domain and would greatly reduce the application cost of the identification of the cross-view target trajectory.

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24

Mambou, Sebastien, Ondrej Krejcar, Kamil Kuca, and Ali Selamat. "Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique." Future Internet 10, no. 9 (September 13, 2018): 89. http://dx.doi.org/10.3390/fi10090089.

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One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques.

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25

Hu, Qian, Wansi Li, Xing Xu, Ning Liu, and Lei Wang. "Learning discriminative representations via variational self-distillation for cross-view geo-localization." Computers and Electrical Engineering 103 (October 2022): 108335. http://dx.doi.org/10.1016/j.compeleceng.2022.108335.

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26

Bnouni, Nesrine, Islem Rekik, Mohamed Salah Rhim, and Najoua Essoukri Ben Amara. "Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer Staging." IEEE Access 7 (2019): 30079–88. http://dx.doi.org/10.1109/access.2019.2902654.

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27

Li, Yandi, Xiping Xu, Jiahong Xu, and Enyu Du. "Bilayer model for cross-view human action recognition based on transfer learning." Journal of Electronic Imaging 28, no. 03 (May 25, 2019): 1. http://dx.doi.org/10.1117/1.jei.28.3.033016.

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28

Jiang, Shuqiang, Weiqing Min, and Shuhuan Mei. "Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction." IEEE Transactions on Multimedia 21, no. 6 (June 2019): 1609–19. http://dx.doi.org/10.1109/tmm.2018.2876830.

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29

Li, Peter Ping. "Toward a learning-based view of internationalization: The accelerated trajectories of cross-border learning for latecomers." Journal of International Management 16, no. 1 (March 2010): 43–59. http://dx.doi.org/10.1016/j.intman.2009.05.003.

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30

Lin, Guoting, Zexun Zheng, Lin Chen, Tianyi Qin, and Jiahui Song. "Multi-Modal 3D Shape Clustering with Dual Contrastive Learning." Applied Sciences 12, no. 15 (July 22, 2022): 7384. http://dx.doi.org/10.3390/app12157384.

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3D shape clustering is developing into an important research subject with the wide applications of 3D shapes in computer vision and multimedia fields. Since 3D shapes generally take on various modalities, how to comprehensively exploit the multi-modal properties to boost clustering performance has become a key issue for the 3D shape clustering task. Taking into account the advantages of multiple views and point clouds, this paper proposes the first multi-modal 3D shape clustering method, named the dual contrastive learning network (DCL-Net), to discover the clustering partitions of unlabeled 3D shapes. First, by simultaneously performing cross-view contrastive learning within multi-view modality and cross-modal contrastive learning between the point cloud and multi-view modalities in the representation space, a representation-level dual contrastive learning module is developed, which aims to capture discriminative 3D shape features for clustering. Meanwhile, an assignment-level dual contrastive learning module is designed by further ensuring the consistency of clustering assignments within the multi-view modality, as well as between the point cloud and multi-view modalities, thus obtaining more compact clustering partitions. Experiments on two commonly used 3D shape benchmarks demonstrate the effectiveness of the proposed DCL-Net.

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31

Wang, Qi, Weidong Min, Qing Han, Ziyuan Yang, Xin Xiong, Meng Zhu, and Haoyu Zhao. "Viewpoint adaptation learning with cross-view distance metric for robust vehicle re-identification." Information Sciences 564 (July 2021): 71–84. http://dx.doi.org/10.1016/j.ins.2021.02.013.

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32

Sholikah, Rizka, Agus Arifin, Chastine Fatichah, and Ayu Purwarianti. "Semantic Relation Detection based on Multi-task Learning and Cross-Lingual-View Embedding." International Journal of Intelligent Engineering and Systems 13, no. 3 (June 30, 2020): 33–45. http://dx.doi.org/10.22266/ijies2020.0630.04.

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33

Schlegel, Claudia, and Roger Kneebone. "Taking a broader view: exploring the materiality of medicine through cross-disciplinary learning." BMJ Simulation and Technology Enhanced Learning 6, no. 2 (November 21, 2018): 108–9. http://dx.doi.org/10.1136/bmjstel-2018-000403.

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34

Li, Ao, Yu Ding, Deyun Chen, Guanglu Sun, Hailong Jiang, and Qidi Wu. "Cross-View Feature Learning via Structures Unlocking Based on Robust Low-Rank Constraint." IEEE Access 8 (2020): 46851–60. http://dx.doi.org/10.1109/access.2020.2978548.

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35

Kuehlkamp, Andrey, Allan Pinto, Anderson Rocha, Kevin W. Bowyer, and Adam Czajka. "Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection." IEEE Transactions on Information Forensics and Security 14, no. 6 (June 2019): 1419–31. http://dx.doi.org/10.1109/tifs.2018.2878542.

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36

Yu, Jun, Xiao-Jun Wu, and Josef Kittler. "Learning discriminative hashing codes for cross-modal retrieval based on multi-view features." Pattern Analysis and Applications 23, no. 3 (February 12, 2020): 1421–38. http://dx.doi.org/10.1007/s10044-020-00870-z.

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37

Wang, Yuanyuan, Xiang Li, Mingxin Jiang, Haiyan Zhang, and E. Tang. "Cross-view pedestrian clustering via graph convolution network for unsupervised person re-identification." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4453–62. http://dx.doi.org/10.3233/jifs-200435.

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At present, supervised person re-identification method achieves high identification performance. However, there are a lot of cross cameras with unlabeled data in the actual application scenarios. The high cost of marking data will greatly reduce the effect of the supervised learning model transferring to other scene domains. Therefore, unsupervised learning of person re-identification becomes more attractive in the real world. In addition, due to changes in camera angle, illumination and posture, the extracted person image representation is generally different in the non-cross camera view, but the existing algorithm ignores the difference among cross camera images under camera parameters and environments. In order to overcome the above problems, we propose unsupervised person re-identification metric learning method. The model learns a shared space to reduce the discrepancy under different cameras. The graph convolution network is further employed to cluster the cross-view image features extracted from the shared space. Our model improves the scalability of pedestrian re-identification in practical application scenarios. Extensive experiments on four large-scale person re-identification public datasets have been conducted to demonstrate the effectiveness of the proposed model.

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38

Zhang, Zhong, and Shuang Liu. "Learning Discriminative Transferable Sparse Coding for Cross-View Action Recognition in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/415021.

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Human action recognition in wireless sensor networks (WSN) is an attractive direction due to its wide applications. However, human actions captured from different sensor nodes in WSN show different views, and the performance of classifier tends to degrade sharply. In this paper, we focus on the issue of cross-view action recognition in WSN and propose a novel algorithm named discriminative transferable sparse coding (DTSC) to overcome the drawback. We learn the sparse representation with an explicit discriminative goal, making the proposed method suitable for recognition. Furthermore, we simultaneously learn the dictionaries from different sensor nodes such that the same actions from different sensor nodes have similar sparse representations. Our method is verified on the IXMAS datasets, and the experimental results demonstrate that our method achieves better results than that of previous methods on cross-view action recognition in WSN.

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39

Zhu, Xiaoke, Xiao-Yuan Jing, Liang Yang, Xinge You, Dan Chen, Guangwei Gao, and Yunhong Wang. "Semi-Supervised Cross-View Projection-Based Dictionary Learning for Video-Based Person Re-Identification." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 10 (October 2018): 2599–611. http://dx.doi.org/10.1109/tcsvt.2017.2718036.

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40

Shen, Xiaobo, Fumin Shen, Quan-Sen Sun, Yang Yang, Yun-Hao Yuan, and Heng Tao Shen. "Semi-Paired Discrete Hashing: Learning Latent Hash Codes for Semi-Paired Cross-View Retrieval." IEEE Transactions on Cybernetics 47, no. 12 (December 2017): 4275–88. http://dx.doi.org/10.1109/tcyb.2016.2606441.

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41

Hajmohammadi, Mohammad Sadegh, Roliana Ibrahim, and Ali Selamat. "Cross-lingual sentiment classification using multiple source languages in multi-view semi-supervised learning." Engineering Applications of Artificial Intelligence 36 (November 2014): 195–203. http://dx.doi.org/10.1016/j.engappai.2014.07.020.

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42

Rosenstein, Alvin, Catherine Sweeney, and Rakesh Gupta. "Cross-Disciplinary Faculty Perspectives On Experiential Learning." Contemporary Issues in Education Research (CIER) 5, no. 3 (July 9, 2012): 139. http://dx.doi.org/10.19030/cier.v5i3.7090.

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An on-line survey was conducted among a universitys department chairs in an effort to gain perspective on university-wide use of Experiential Learning (EL). While there were differences in cross-disciplinary definitions and perspectives regarding EL, ninety-one per cent of 35 department chairs indicated their department made use of EL with greatest use during the junior and senior years. EL is defined generally as a hands-on experience and/or learning by doing while cognitive activity, such as observation and reflection, is included in the definition by a third of the chairs. Eighty-eight per cent of the chairs believe students view EL as either very beneficial or beneficial.

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43

Chwialkowska, Agnieszka. "Maximizing Cross-Cultural Learning From Exchange Study Abroad Programs: Transformative Learning Theory." Journal of Studies in International Education 24, no. 5 (February 20, 2020): 535–54. http://dx.doi.org/10.1177/1028315320906163.

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While some institutions require their students to spend a semester abroad as a prerequisite to earning a business degree, academics challenge the view that travel abroad helps students become culturally competent. Many students admit that they failed to immerse themselves in a cross-cultural environment. Therefore, the purpose of this study is to identify the components of exchange study abroad programs (ESP) that facilitate student cross-cultural learning (CCL). Building on transformative learning theory (TLT), we propose and test a conceptual model of relationships between different components of exchange programs and student CCL. The data collected from more than 700 students participating in a semester and two-semester-long programs are analyzed through logistic regression. This research contributes to the literature on the effectiveness of ESP by identifying the key components that maximize positive outcomes for students. By building on TLT, it reveals the importance of getting out of one’s comfort zone and providing students with support during the ESP. This study bears practical implications as it provides academic institutions and students with important insights that help maximize student CCL.

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44

Luo, Yadong. "Toward a reverse adaptation view in cross-cultural management." Cross Cultural & Strategic Management 23, no. 1 (February 1, 2016): 29–41. http://dx.doi.org/10.1108/ccsm-08-2015-0102.

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Purpose – Contrasting local adaptation, which focusses on foreign multinationals learning about and adapting to local (host country) culture and environment, reverse adaptation refers to the case where an MNE’s local employees learn, assimilate and modify their personal behavior (e.g. values, norms) and professional competence (e.g. standards, goals, language, knowledge, capabilities) in order to fit the MNE’s global mindset and global competence set so that they can be internationally reassigned. The purpose of this paper is to take the first step toward addressing this nascent phenomenon and practice. Design/methodology/approach – This study uses combined inductive and ethnographic methods to explore the importance, process and practice of reverse innovation. This study defines reverse adaptation, illustrates the major driving forces underlying reverse adaptation, and suggests how MNEs should prepare for it. As reverse adaptation is a promising area for research, this paper also proposes a research agenda for international management scholars. Findings – MNEs need to act at both local and global levels in a way that recognizes the interdependence between the two. Too often global companies have approached their local talent needs in an uncoordinated and unproductive way. Reverse adaptation view suggests that MNEs can create a competitive advantage by taking a global approach to talent. Cultivating and transforming local talent to become global talent necessitates endeavor from a wide range of corporate, subsidiary and individual levels, in cultural, professional, structural, informational and organizational aspects. Originality/value – Reverse adaptation is a promising area of research because it provides the opportunity to enrich mainstream theories and literatures in a number of areas. This nascent phenomenon has not yet been studied, and this paper represents the first effort to do so. From both academic and practice viewpoints, reverse adaptation has a significant impact on global talent management, knowledge flow across borders, capability catchup and global integration design. Today’s glocalized business world, with heightened integration of world economy, creates an expectation for the continuing growth of reverse adaptation.

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45

Lan, Yizhou, and Will X. Y. Li. "Personality, Category, and Cross-Linguistic Speech Sound Processing: A Connectivistic View." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/586504.

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Category formation of human perception is a vital part of cognitive ability. The disciplines of neuroscience and linguistics, however, seldom mention it in the marrying of the two. The present study reviews the neurological view of language acquisition as normalization of incoming speech signal, and attempts to suggest how speech sound category formation may connect personality with second language speech perception. Through a questionnaire, (being thick or thin) ego boundary, a correlate found to be related to category formation, was proven a positive indicator of personality types. Following the qualitative study, thick boundary and thin boundary English learners native in Cantonese were given a speech-signal perception test using an ABX discrimination task protocol. Results showed that thick-boundary learners performed significantly lower in accuracy rate than thin-boundary learners. It was implied that differences in personality do have an impact on language learning.

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46

Zhang, Hong, Xingyu Gao, Ping Wu, and Xin Xu. "A cross-media distance metric learning framework based on multi-view correlation mining and matching." World Wide Web 19, no. 2 (April 21, 2015): 181–97. http://dx.doi.org/10.1007/s11280-015-0342-4.

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Zhao, Xiantong, and Xu Liu. "Academic Visits as Transformative Learning Opportunities: The Case of Chinese Visiting Academics." SAGE Open 12, no. 4 (October 2022): 215824402211347. http://dx.doi.org/10.1177/21582440221134795.

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Cross-border academic visits by university faculty members are becoming prevalent globally. Unlike previous research, which has focused on the cross-cultural adaptation arising from the cross-border movement of people, we view scholars’ visiting experiences as a learning opportunity in light of Mezirow’s transformative learning theory (TLT). We employ Addleman et al.’s three-stage proposal to better understand the transformative learning process of Chinese visiting scholars. Drawing on Hoggan’s typology, we identify changes in scholars’ worldviews, selves, and behavior as outcomes of transformative learning. We conclude that international experience is beneficial for scholars and call for more study abroad opportunities for Chinese university faculty.

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Masturin, Masturin. "Cross-Cultural Counseling as an Effective and Efficient Approach for Students." KONSELING RELIGI Jurnal Bimbingan Konseling Islam 11, no. 2 (November 10, 2020): 341. http://dx.doi.org/10.21043/kr.v11i2.8566.

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This research aims to produce an effective and efficient, participatory, creative, and fun learning process. The learning strategy is also a plan that contains a series of activities designed to achieve certain educational goals and as a starting point or point of view towards the learning process. The term approach refers to the view on occurrence of a process which is still very general in nature. Therefore, the strategies, approaches and learning methods used can be sources or depend on certain approaches. Two approaches are often used to learning, namely the teacher-centered approach and the student-centered approach. This study used a descriptive qualitative approach, taking primary and secondary data sources by interviewing, observing, documenting, the analyzing and testing the data with transferability, dependability, and confirmability. The result of this study indicate that learning with a cross-cultural counseling approach is very effective and efficient for the success of students in learning process because there is no difference between them and what happens is mutual understanding of culture, mutual assistance, tolerance, and understanding of each other’s interests.

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Geng, Wanxuan, Weixun Zhou, and Shuanggen Jin. "Multi-View Urban Scene Classification with a Complementary-Information Learning Model." Photogrammetric Engineering & Remote Sensing 88, no. 1 (January 1, 2022): 65–72. http://dx.doi.org/10.14358/pers.21-00062r2.

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Traditional urban scene-classification approaches focus on images taken either by satellite or in aerial view. Although single-view images are able to achieve satisfactory results for scene classification in most situations, the complementary information provided by other image views is needed to further improve performance. Therefore, we present a complementary information-learning model (CILM) to perform multi-view scene classification of aerial and ground-level images. Specifically, the proposed CILM takes aerial and ground-level image pairs as input to learn view-specific features for later fusion to integrate the complementary information. To train CILM, a unified loss consisting of cross entropy and contrastive losses is exploited to force the network to be more robust. Once CILM is trained, the features of each view are extracted via the two proposed feature-extraction scenarios and then fused to train the support vector machine classifier for classification. The experimental results on two publicly available benchmark data sets demonstrate that CILM achieves remarkable performance, indicating that it is an effective model for learning complementary information and thus improving urban scene classification.

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Williams, Roy, Simone Gumtau, and Jenny Mackness. "Synesthesia: From Cross-Modal to Modality-Free Learning and Knowledge." Leonardo 48, no. 1 (February 2015): 48–54. http://dx.doi.org/10.1162/leon_a_00937.

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In an integrated view of perception and action, learning involves all the senses, their interaction and cross-modality, rather than multi-modality alone. This can be referred to as synesthetic enactive perception, which forms the basis for more abstract, modality-free knowledge and a potential underpinning for innovative learning design. The authors explore this mode of learning in two case studies: The first focuses on children in Montessori preschools and the second on MEDIATE, an interactive space designed for children on the autistic spectrum that offers a “whole-body” engagement with the world.

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