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Recent methods for video action recognition have reached outstanding performances on existing benchmarks.
Zach C, Pock T, Bischof H (2007) A duality based approach for realtime TV-L1 optical flow. In: Joint pattern recognition symposium
2007
Earlier work this paper cites.
Kuehne H, Jhuang H, Garrote E, Poggio T, Serre T (2011) HMDB: a large video database for human motion recognition. In: ICCV
2011
Earlier work this paper cites.
Torralba A, Efros AA, et al. (2011) Unbiased look at dataset bias. In: CVPR
2011
Earlier work this paper cites.
Khosla A, Zhou T, Malisiewicz T, Efros AA, Torralba A (2012) Undoing the damage of dataset bias. In: ECCV
2012
Earlier work this paper cites.
Soomro K, Zamir AR, Shah M (2012) UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild. In: CRCV-TR-12-01
2012
Earlier work this paper cites.
Yao A, Gall J, van Gool L (2012) Coupled action recognition and pose estimation from multiple views. IJCV
2012
Earlier work this paper cites.
Jhuang H, Gall J, Zuffi S, Schmid C, Black MJ (2013) Towards understanding action recognition. In: ICCV
2013
Earlier work this paper cites.
Zhang W, Zhu M, Derpanis KG (2013) From actemes to action: A strongly-supervised representation for detailed action understanding. In: ICCV
2013
Earlier work this paper cites.
Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: NIPS
2014
Earlier work this paper cites.
Chéron G, Laptev I, Schmid C (2015) P-CNN: Pose-based CNN features for action recognition. In: ICCV
2015
Earlier work this paper cites.
Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: CVPR
2015
Earlier work this paper cites.
Gkioxari G, Malik J (2015) Finding action tubes. In: CVPR
2015
Earlier work this paper cites.
Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS
2015
Earlier work this paper cites.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: CVPR
2015
Earlier work this paper cites.
Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: ICCV
2015
Earlier work this paper cites.
Weinzaepfel P, Harchaoui Z, Schmid C (2015) Learning to track for spatio-temporal action localization. In: ICCV
2015
Earlier work this paper cites.
Cao C, Zhang Y, Zhang C, Lu H (2016) Action recognition with joints-pooled 3D deep convolutional descriptors. In: IJCAI
2016
Earlier work this paper cites.
Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: CVPR
2016
Earlier work this paper cites.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR
2016
Earlier work this paper cites.
Liu J, Shahroudy A, Xu D, Wang G (2016) Spatio-temporal LSTM with trust gates for 3D human action recognition. In: ECCV
2016
Cited alongside, same era.
Saha S, Singh G, Sapienza M, Torr PH, Cuzzolin F (2016) Deep learning for detecting multiple space-time action tubes in videos. In: BMVC
2016
Cited alongside, same era.
Shahroudy A, Liu J, Ng TT, Wang G (2016) NTU RGB+D: A large scale dataset for 3D human activity analysis. In: CVPR
2016
Cited alongside, same era.
Wang L, Xiong Y, Wang Z, Qiao Y, Lin D, Tang X, Van Gool L (2016) Temporal segment networks: Towards good practices for deep action recognition. In: ECCV
2016
Cited alongside, same era.
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: CVPR
2016
Cited alongside, same era.
Hara K, Kataoka H, Satoh Y (2018) Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet. In: CVPR
2018
Later among the works it cites.
Li Y, Li Y, Vasconcelos N (2018) Resound: Towards action recognition without representation bias. In: ECCV
2018
Later among the works it cites.
Liu M, Yuan J (2018) Recognizing human actions as the evolution of pose estimation maps. In: CVPR
2018
Later among the works it cites.
Luvizon DC, Picard D, Tabia H (2018) 2D/3D pose estimation and action recognition using multitask deep learning. In: CVPR
2018
Later among the works it cites.
Sevilla-Lara L, Liao Y, Güney F, Jampani V, Geiger A, Black MJ (2018) On the integration of optical flow and action recognition. In: GCPR
2018
Later among the works it cites.
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Zhu W, Lan C, Xing J, Li Y, Shen L, Zeng W, Xie X (2016) Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks. In: AAAI
2016
Cited alongside, same era.
Carreira J, Zisserman A (2017) Quo vadis, action recognition? A new model and the Kinetics dataset. In: CVPR
2017
Cited alongside, same era.
Du W, Wang Y, Qiao Y (2017) RPAN: An end-to-end recurrent pose-attention network for action recognition in videos. In: ICCV
2017
Cited alongside, same era.
Girdhar R, Ramanan D (2017) Attentional pooling for action recognition. In: NIPS
2017
Cited alongside, same era.
Iqbal U, Garbade M, Gall J (2017) Pose for action-action for pose. In: International Conference on Automatic Face & Gesture Recognition (FG)
2017
Cited alongside, same era.
Kalogeiton V, Weinzaepfel P, Ferrari V, Schmid C (2017) Action tubelet detector for spatio-temporal action localization. In: ICCV
2017
Cited alongside, same era.
Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Back T, Natsev P, et al. (2017) The Kinetics human action video dataset. arXiv preprint arXiv:170506950
2017
Cited alongside, same era.
Si C, Jing Y, Wang W, Wang L, Tan T (2018) Skeleton-based action recognition with spatial reasoning and temporal stack learning. In: ECCV
2018
Later among the works it cites.
Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M (2018) A closer look at spatiotemporal convolutions for action recognition. In: CVPR
2018
Later among the works it cites.
Wang W, Zhang J, Si C, Wang L (2018) Pose-based two-stream relational networks for action recognition in videos. arXiv preprint arXiv:180508484
2018
Later among the works it cites.
Weng J, Liu M, Jiang X, Yuan J (2018) Deformable pose traversal convolution for 3d action and gesture recognition. In: ECCV
2018
Later among the works it cites.
Xie S, Sun C, Huang J, Tu Z, Murphy K (2018) Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. In: ECCV
2018
Later among the works it cites.
Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: AAAI
2018
Later among the works it cites.
Zhu J, Zou W, Xu L, Hu Y, Zhu Z, Chang M, Huang J, Huang G, Du D (2018) Action machine: Rethinking action recognition in trimmed videos. arXiv preprint arXiv:181205770
2018
Later among the works it cites.
Bahng H, Chun S, Yun S, Choo J, Oh SJ (2019) Learning de-biased representations with biased representations. arXiv
2019
Closest in time.
Feichtenhofer C, Fan H, Malik J, He K (2019) Slowfast networks for video recognition. In: ICCV
2019
Closest in time.
Ghadiyaram D, Tran D, Mahajan D (2019) Large-scale weakly-supervised pre-training for video action recognition. In: CVPR
2019
Closest in time.
Li Y, Vasconcelos N (2019) Repair: Removing representation bias by dataset resampling. In: CVPR
2019
Closest in time.
McNally W, Wong A, McPhee J (2019) STAR-Net: Action recognition using spatio-temporal activation reprojection. In: CRV
2019
Closest in time.
Rogez G, Weinzaepfel P, Schmid C (2019) LCR-Net++: Multi-person 2D and 3D pose detection in natural images. IEEE trans PAMI
2019
Closest in time.
Jacquot V, Ying Z, Kreiman G (2020) Can deep learning recognize subtle human activities. In: CVPR
2020
Closest in time.