Luojun Lin
Luojun Lin
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Scut-fbp5500: A diverse benchmark dataset for multi-paradigm facial beauty prediction
L Liang, L Lin, L Jin, D Xie, M Li
2018 24th International Conference on Pattern Recognition (ICPR), 1598-1603, 2018
Action recognition using attention-joints graph convolutional neural networks
T Ahmad, H Mao, L Lin, G Tang
IEEE Access 8, 305-313, 2019
SynSig2Vec: Learning representations from synthetic dynamic signatures for real-world verification
S Lai, L Jin, L Lin, Y Zhu, H Mao
Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 735-742, 2020
Regression guided by relative ranking using convolutional neural network (R3CNN) for facial beauty prediction
L Lin, L Liang, L Jin
IEEE Transactions on Affective Computing, 2019
Attribute-Aware Convolutional Neural Networks for Facial Beauty Prediction.
L Lin, L Liang, L Jin, W Chen
IJCAI, 847-853, 2019
Region-aware scattering convolution networks for facial beauty prediction
L Liang, D Xie, L Jin, J Xu, M Li, L Lin
2017 IEEE International Conference on Image Processing (ICIP), 2861-2865, 2017
R2-ResNeXt: A ResNeXt-Based Regression Model with Relative Ranking for Facial Beauty Prediction
L Lin, L Liang, L Jin
2018 24th International Conference on Pattern Recognition (ICPR), 85-90, 2018
Skeleton-based action recognition using sparse spatio-temporal GCN with edge effective resistance
T Ahmad, L Jin, L Lin, GZ Tang
Neurocomputing 423, 389-398, 2021
SynSig2Vec: Forgery-free Learning of Dynamic Signature Representations by Sigma Lognormal-based Synthesis
S Lai, L Jin, Y Zhu, Z Li, L Lin
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Unsupervised Image Classification for Deep Representation Learning
W Chen, S Pu, D Xie, S Yang, Y Guo, L Lin
European Conference on Computer Vision, 430-446, 2020
Graph Convolutional Neural Network for Action Recognition: A Comprehensive Survey
T Ahmad, L Jin, X Zhang, L Lin, G Tang
IEEE Transactions on Artificial Intelligence, 2021
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
W Chen, L Lin, S Yang, D Xie, S Pu, Y Zhuang, W Ren
arXiv preprint arXiv:2102.11614, 2021
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