3D Convolutional Neural Networks for Human Action Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.1, pp.3-221, 2013. ,
DOI : 10.1109/TPAMI.2012.59
Largescale video classification with convolutional neural networks, In: CVPR, pp.1725-1732, 2014. ,
Fast implementation of sparse iterative covariance-based estimation for source localization, The Journal of the Acoustical Society of America, vol.131, issue.2, pp.1249-1259, 2012. ,
DOI : 10.1121/1.3672656
Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images, Sensors, vol.12, issue.6, 2017. ,
DOI : 10.1016/j.patrec.2005.10.010
Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing, IEEE Transactions on Signal Processing, vol.61, issue.4, pp.933-944, 2013. ,
DOI : 10.1109/TSP.2012.2231676
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4724-4733, 2017. ,
DOI : 10.1109/CVPR.2017.502
, Automatic Salient Object Extraction with Contextual Cue In: ICCV, pp.105-112, 2011.
Video object discovery and co-segmentation with extremely weak supervision, pp.2074-2088, 2017. ,
Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3431-3440, 2015. ,
DOI : 10.1109/CVPR.2015.7298965
Two-stream convolutional networks for action recognition in videos, In: NIPS, pp.568-576, 2014. ,
Beyond short snippets: Deep networks for video classification, In: CVPR, pp.4694-4702, 2015. ,
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition, pp.20-36, 2016. ,
DOI : 10.1109/CVPR.2016.219
Long-term recurrent convolutional networks for visual recognition and description, pp.2625-2634, 2015. ,
Learning Spatiotemporal Features with 3D Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), pp.4489-4497, 2015. ,
DOI : 10.1109/ICCV.2015.510
P-CNN: Pose-Based CNN Features for Action Recognition, 2015 IEEE International Conference on Computer Vision (ICCV), pp.3218-3226, 2015. ,
DOI : 10.1109/ICCV.2015.368
Convolutional Two-Stream Network Fusion for Video Action Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1933-1941, 2016. ,
DOI : 10.1109/CVPR.2016.213
URL : http://arxiv.org/pdf/1604.06573
Video-based sign language recognition without temporal segmentation. arXiv preprint, 2018. ,
Action Recognition with Improved Trajectories, 2013 IEEE International Conference on Computer Vision, pp.3551-3558, 2013. ,
DOI : 10.1109/ICCV.2013.441
URL : https://hal.archives-ouvertes.fr/hal-00873267
Recognizing human actions: a local SVM approach, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.32-36, 2004. ,
DOI : 10.1109/ICPR.2004.1334462
URL : http://www.nada.kth.se/%7Ecaputo/publik/icpr04actions.pdf
Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv, pp.1212-0402, 2012. ,
HMDB51: A Large Video Database for Human Motion Recognition, In: High Performance Computing in Science and Engineering, pp.571-582, 2013. ,
DOI : 10.1007/978-3-642-33374-3_41
URL : http://cbcl.mit.edu/publications/ps/Kuehne_etal_iccv11.pdf
Effective Approaches to Attention-based Neural Machine Translation, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015. ,
DOI : 10.18653/v1/D15-1166
URL : http://arxiv.org/pdf/1508.04025
Show, attend and tell: Neural image caption generation with visual attention, pp.2048-2057, 2015. ,
Recurrent models of visual attention, In: NIPS, pp.2204-2212, 2014. ,
Action recognition by dense trajectories, CVPR 2011, pp.3169-3176, 2011. ,
DOI : 10.1109/CVPR.2011.5995407
URL : https://hal.archives-ouvertes.fr/inria-00583818
On Space-Time Interest Points, International Journal of Computer Vision, vol.17, issue.8, pp.107-123, 2005. ,
DOI : 10.1007/BFb0017862
URL : http://kth.diva-portal.org/smash/get/diva2:442088/FULLTEXT01
A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features, Sensors, vol.12, issue.10, 2017. ,
DOI : 10.1109/TGRS.2011.2153861
URL : https://doi.org/10.3390/s17102421
Mining actionlet ensemble for action recognition with depth cameras, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.1290-1297, 2012. ,
DOI : 10.1109/CVPR.2012.6247813
Hierarchical recurrent neural network for skeleton based action recognition, In: CVPR, pp.1110-1118, 2015. ,
Multi-View Visual Recognition of Imperfect Testing Data, Proceedings of the 23rd ACM international conference on Multimedia, MM '15, pp.561-570, 2015. ,
DOI : 10.1145/2020408.2020593
Can Visual Recognition Benefit from Auxiliary Information in Training?, Lecture Notes in Computer Science, vol.9003, pp.65-80, 2015. ,
DOI : 10.1007/978-3-319-16865-4_5
URL : http://www.cs.stevens.edu/%7Eghua/publication/ACCV14b.pdf
Auxiliary Training Information Assisted Visual Recognition, IPSJ Transactions on Computer Vision and Applications, vol.7, issue.0, pp.138-150, 2015. ,
DOI : 10.2197/ipsjtcva.7.138
URL : https://www.jstage.jst.go.jp/article/ipsjtcva/7/0/7_138/_pdf
Describing Videos by Exploiting Temporal Structure, 2015 IEEE International Conference on Computer Vision (ICCV), pp.4507-4515, 2015. ,
DOI : 10.1109/ICCV.2015.512
URL : http://arxiv.org/pdf/1502.08029
Temporal Localization of Actions with Actoms, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.11, pp.2782-2795, 2013. ,
DOI : 10.1109/TPAMI.2013.65
URL : https://hal.archives-ouvertes.fr/hal-00687312
Fast implementation of sparse iterative covariance-based estimation for array processing, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp.2031-2035, 2011. ,
DOI : 10.1109/ACSSC.2011.6190383
Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016. ,
DOI : 10.1109/CVPR.2016.90
URL : http://arxiv.org/pdf/1512.03385
Batch normalization: Accelerating deep network training by reducing internal covariate shift, In: ICML, pp.448-456, 2015. ,
ImageNet: A large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.248-255, 2009. ,
DOI : 10.1109/CVPR.2009.5206848
, , p.Pytorch, 2017.
Multi-view Super Vector for Action Recognition, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.596-603, 2014. ,
DOI : 10.1109/CVPR.2014.83
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice, Computer Vision and Image Understanding, vol.150, pp.109-125, 2016. ,
DOI : 10.1016/j.cviu.2016.03.013
MoFAP: A Multi-level Representation for Action Recognition, International Journal of Computer Vision, vol.23, issue.2, pp.254-271, 2016. ,
DOI : 10.1109/ICCV.2013.442
Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), pp.4597-4605, 2015. ,
DOI : 10.1109/ICCV.2015.522
Action recognition with trajectory-pooled deepconvolutional descriptors, In: CVPR, pp.4305-4314, 2015. ,
Long-Term Temporal Convolutions for Action Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, issue.6, 2017. ,
DOI : 10.1109/TPAMI.2017.2712608
URL : https://hal.archives-ouvertes.fr/hal-01241518
A Key Volume Mining Deep Framework for Action Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1991-1999, 2016. ,
DOI : 10.1109/CVPR.2016.219
Modeling video evolution for action recognition, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5378-5387, 2015. ,
DOI : 10.1109/CVPR.2015.7299176
Motion Part Regularization: Improving action recognition via trajectory group selection, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3698-3706, 2015. ,
DOI : 10.1109/CVPR.2015.7298993