Temporal Attentive Alignment for Large-Scale Video Domain Adaptation
暂无分享,去创建一个
Ruxin Chen | Ghassan Al-Regib | Jian Zheng | Zsolt Kira | Min-Hung Chen | Jaekwon Yoo | Z. Kira | Ruxin Chen | G. Al-Regib | Min-Hung Chen | J. Yoo | Jian Zheng | Jaekwon Yoo
[1] Jonathan Tompson,et al. Temporal Cycle-Consistency Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[3] Xiao Liu,et al. Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Dong Xu,et al. Collaborative and Adversarial Network for Unsupervised Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Edward K. Wong,et al. Dual many-to-one-encoder-based transfer learning for cross-dataset human action recognition , 2016, Image Vis. Comput..
[7] Luc Van Gool,et al. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition , 2016, ECCV.
[8] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[9] Marcin Andrychowicz,et al. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[10] Fabio Viola,et al. The Kinetics Human Action Video Dataset , 2017, ArXiv.
[11] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[12] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[13] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[14] Zsolt Kira,et al. Learning to cluster in order to Transfer across domains and tasks , 2017, ICLR.
[15] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[16] Yann LeCun,et al. A Closer Look at Spatiotemporal Convolutions for Action Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[17] Fei-Fei Li,et al. Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Changsheng Li,et al. Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision , 2019, AAAI.
[20] Razvan Pascanu,et al. A simple neural network module for relational reasoning , 2017, NIPS.
[21] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[22] Kate Saenko,et al. Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation , 2018, ArXiv.
[23] Geoffrey French,et al. Self-ensembling for visual domain adaptation , 2017, ICLR.
[24] Qilong Wang,et al. Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[26] Jiaying Liu,et al. Adaptive Batch Normalization for practical domain adaptation , 2018, Pattern Recognit..
[27] Allan Jabri,et al. Learning Correspondence From the Cycle-Consistency of Time , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Jiaying Liu,et al. Revisiting Batch Normalization For Practical Domain Adaptation , 2016, ICLR.
[29] Bolei Zhou,et al. Temporal Relational Reasoning in Videos , 2017, ECCV.
[30] Zhao Chen,et al. GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks , 2017, ICML.
[31] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[32] Andrew Zisserman,et al. Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.
[33] Andrew Zisserman,et al. A Short Note about Kinetics-600 , 2018, ArXiv.
[34] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[35] Andrew Zisserman,et al. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Jianmin Wang,et al. Transferable Attention for Domain Adaptation , 2019, AAAI.
[37] Trevor Darrell,et al. Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Chen-Yu Lee,et al. Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Tao Mei,et al. Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[40] K. S. Venkatesh,et al. Deep Domain Adaptation in Action Space , 2018, BMVC.
[41] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[42] Edwin Lughofer,et al. Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning , 2017, ICLR.
[43] Roberto Cipolla,et al. Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[44] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[45] Andrew Zisserman,et al. Convolutional Two-Stream Network Fusion for Video Action Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Kate Saenko,et al. Adversarial Dropout Regularization , 2017, ICLR.
[47] Imran Saleemi,et al. Human Action Recognition across Datasets by Foreground-Weighted Histogram Decomposition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[48] Asim Kadav,et al. Attend and Interact: Higher-Order Object Interactions for Video Understanding , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[49] Lorenzo Torresani,et al. Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[50] Mubarak Shah,et al. UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.
[51] Gabriela Csurka,et al. A Comprehensive Survey on Domain Adaptation for Visual Applications , 2017, Domain Adaptation in Computer Vision Applications.
[52] Thomas Serre,et al. HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.
[53] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[54] Tatsuya Harada,et al. Open Set Domain Adaptation by Backpropagation , 2018, ECCV.
[55] Juergen Gall,et al. Open Set Domain Adaptation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[56] Ghassan Al-Regib,et al. TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition , 2017, Signal Process. Image Commun..
[57] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .