Learning Discriminative Video Representations Using Adversarial Perturbations
暂无分享,去创建一个
[1] Xiaohui Xie,et al. Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks , 2016, AAAI.
[2] Lorenzo Torresani,et al. Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[3] William W. Hager,et al. A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search , 2005, SIAM J. Optim..
[4] Matthew J. Hausknecht,et al. Beyond short snippets: Deep networks for video classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Austin Reiter,et al. Interpretable 3D Human Action Analysis with Temporal Convolutional Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[6] Anoop Cherian,et al. Video Representation Learning Using Discriminative Pooling , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Gang Wang,et al. NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Richard P. Wildes,et al. Spatiotemporal Residual Networks for Video Action Recognition , 2016, NIPS.
[9] W. Boothby. An introduction to differentiable manifolds and Riemannian geometry , 1975 .
[10] Nitish Srivastava,et al. Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.
[11] Tinne Tuytelaars,et al. Modeling video evolution for action recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Gang Wang,et al. Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Yann LeCun,et al. Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[14] 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).
[15] Mohammed Bennamoun,et al. Deep Reconstruction Models for Image Set Classification , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Sanghoon Lee,et al. Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[17] Xilin Chen,et al. Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Levent Tunçel,et al. Optimization algorithms on matrix manifolds , 2009, Math. Comput..
[19] Lin Sun,et al. Lattice Long Short-Term Memory for Human Action Recognition , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[20] 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).
[21] Ali Razavi,et al. Data-Efficient Image Recognition with Contrastive Predictive Coding , 2019, ICML.
[22] Cordelia Schmid,et al. Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.
[23] Thomas Serre,et al. HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.
[24] Anoop Cherian,et al. On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization , 2016, ArXiv.
[25] Seong Joon Oh,et al. Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[26] Ramakant Nevatia,et al. DISCOVER: Discovering Important Segments for Classification of Video Events and Recounting , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[27] Tao Mei,et al. Action Recognition by Learning Deep Multi-Granular Spatio-Temporal Video Representation , 2016, ICMR.
[28] Nanning Zheng,et al. View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[29] Hakan Bilen,et al. Explorer Action Recognition with Dynamic Image Networks , 2017 .
[30] Jason J. Corso,et al. Action bank: A high-level representation of activity in video , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[31] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[33] Steven Thomas Smith,et al. Optimization Techniques on Riemannian Manifolds , 2014, ArXiv.
[34] Basura Fernando,et al. Learning End-to-end Video Classification with Rank-Pooling , 2016, ICML.
[35] Gang Wang,et al. Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition , 2016, ECCV.
[36] Jessica Schulze,et al. Fundamental Methods Of Mathematical Economics , 2016 .
[37] Jing Zhang,et al. Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[38] R Devon Hjelm,et al. Learning Representations by Maximizing Mutual Information Across Views , 2019, NeurIPS.
[39] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[40] Luc Van Gool,et al. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition , 2016, ECCV.
[41] Anoop Cherian,et al. Non-linear Temporal Subspace Representations for Activity Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[42] Yi Yang,et al. Semantic Pooling for Complex Event Analysis in Untrimmed Videos , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Hongdong Li,et al. Expanding the Family of Grassmannian Kernels: An Embedding Perspective , 2014, ECCV.
[44] Anoop Cherian,et al. Ordered Pooling of Optical Flow Sequences for Action Recognition , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[45] Christopher Joseph Pal,et al. Delving Deeper into Convolutional Networks for Learning Video Representations , 2015, ICLR.
[46] Yong Du,et al. Hierarchical recurrent neural network for skeleton based action recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Andrew Zisserman,et al. Convolutional Two-Stream Network Fusion for Video Action Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Seyed-Mohsen Moosavi-Dezfooli,et al. Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Bamdev Mishra,et al. Manopt, a matlab toolbox for optimization on manifolds , 2013, J. Mach. Learn. Res..
[50] Oriol Vinyals,et al. Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.
[51] Christian Wolf,et al. Sequential Deep Learning for Human Action Recognition , 2011, HBU.
[52] Marcus Hutter,et al. Discriminative Hierarchical Rank Pooling for Activity Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Mehrtash Tafazzoli Harandi,et al. From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices , 2014, ECCV.
[54] Alan L. Yuille,et al. Adversarial Examples for Semantic Segmentation and Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[55] Wei Zeng,et al. Learning Long-Term Dependencies for Action Recognition with a Biologically-Inspired Deep Network , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[56] David A. Forsyth,et al. SafetyNet: Detecting and Rejecting Adversarial Examples Robustly , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[57] Phillip Isola,et al. Contrastive Multiview Coding , 2019, ECCV.
[58] Richard P. Wildes,et al. Temporal Residual Networks for Dynamic Scene Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] S. P. Mudur,et al. Three-dimensional computer vision: a geometric viewpoint , 1993 .
[60] Andrew Zisserman,et al. Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.
[61] Andrea Vedaldi,et al. Dynamic Image Networks for Action Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[62] Cordelia Schmid,et al. Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.
[63] Richard P. Wildes,et al. Spatiotemporal Multiplier Networks for Video Action Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[64] Anoop Cherian,et al. Generalized Rank Pooling for Activity Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[65] Chengxu Zhuang,et al. Local Aggregation for Unsupervised Learning of Visual Embeddings , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[66] Yoshua Bengio,et al. Learning deep representations by mutual information estimation and maximization , 2018, ICLR.
[67] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[68] Hao Wang,et al. Hierarchical Dynamic Parsing and Encoding for Action Recognition , 2016, ECCV.