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Xiaojun Chang
Xiaojun Chang
Director of The ReLER Lab and Professor in Artificial Intelligence, University of Technology Sydney
Bestätigte E-Mail-Adresse bei uts.edu.au - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Connecting the dots: Multivariate time series forecasting with graph neural networks
Z Wu, S Pan, G Long, J Jiang, X Chang, C Zhang
Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020
15342020
A survey of deep active learning
P Ren, Y Xiao, X Chang, PY Huang, Z Li, BB Gupta, X Chen, X Wang
ACM computing surveys (CSUR) 54 (9), 1-40, 2021
13372021
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
P Ren, Y Xiao, X Chang, PY Huang, Z Li, X Chen, X Wang
ACM Computing Surveys 54 (4), 1-34, 2021
7422021
Multi-class active learning by uncertainty sampling with diversity maximization
Y Yang, Z Ma, F Nie, X Chang, AG Hauptmann
International Journal of Computer Vision 113, 113-127, 2015
5402015
Hierarchical neural architecture search for deep stereo matching
X Cheng, Y Zhong, M Harandi, Y Dai, X Chang, H Li, T Drummond, Z Ge
Advances in neural information processing systems 33, 22158-22169, 2020
3792020
A semisupervised recurrent convolutional attention model for human activity recognition
K Chen, L Yao, D Zhang, X Wang, X Chang, F Nie
IEEE transactions on neural networks and learning systems 31 (5), 1747-1756, 2019
3662019
Semantic pooling for complex event analysis in untrimmed videos
X Chang, YL Yu, Y Yang, EP Xing
IEEE transactions on pattern analysis and machine intelligence 39 (8), 1617-1632, 2017
3532017
Making sense of spatio-temporal preserving representations for EEG-based human intention recognition
D Zhang, L Yao, K Chen, S Wang, X Chang, Y Liu
IEEE transactions on cybernetics 50 (7), 3033-3044, 2019
3422019
A comprehensive survey of scene graphs: Generation and application
X Chang, P Ren, P Xu, Z Li, X Chen, A Hauptmann
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (1), 1-26, 2021
3352021
An adaptive semisupervised feature analysis for video semantic recognition
M Luo, X Chang, L Nie, Y Yang, AG Hauptmann, Q Zheng
IEEE transactions on cybernetics 48 (2), 648-660, 2017
3282017
A convex formulation for semi-supervised multi-label feature selection
X Chang, F Nie, Y Yang, H Huang
Proceedings of the AAAI conference on artificial intelligence 28 (1), 2014
2932014
Rank-constrained spectral clustering with flexible embedding
Z Li, F Nie, X Chang, L Nie, H Zhang, Y Yang
IEEE transactions on neural networks and learning systems 29 (12), 6073-6082, 2018
2722018
Dynamic affinity graph construction for spectral clustering using multiple features
Z Li, F Nie, X Chang, Y Yang, C Zhang, N Sebe
IEEE transactions on neural networks and learning systems 29 (12), 6323-6332, 2018
2702018
Vision-language navigation with self-supervised auxiliary reasoning tasks
F Zhu, Y Zhu, X Chang, X Liang
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
2662020
Self-supervised deep correlation tracking
D Yuan, X Chang, PY Huang, Q Liu, Z He
IEEE Transactions on Image Processing 30, 976-985, 2020
2622020
Bi-level semantic representation analysis for multimedia event detection
X Chang, Z Ma, Y Yang, Z Zeng, AG Hauptmann
IEEE transactions on cybernetics 47 (5), 1180-1197, 2016
2462016
Semisupervised feature analysis by mining correlations among multiple tasks
X Chang, Y Yang
IEEE transactions on neural networks and learning systems 28 (10), 2294-2305, 2016
2452016
Adaptive unsupervised feature selection with structure regularization
M Luo, F Nie, X Chang, Y Yang, AG Hauptmann, Q Zheng
IEEE transactions on neural networks and learning systems 29 (4), 944-956, 2017
2402017
MMALFM: Explainable recommendation by leveraging reviews and images
Z Cheng, X Chang, L Zhu, RC Kanjirathinkal, M Kankanhalli
ACM Transactions on Information Systems (TOIS) 37 (2), 1-28, 2019
2382019
Block-wisely supervised neural architecture search with knowledge distillation
C Li, J Peng, L Yuan, G Wang, X Liang, L Lin, X Chang
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
2322020
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