Weakly-Supervised Completion Moment Detection using Temporal Attention
暂无分享,去创建一个
Majid Mirmehdi | Dima Damen | Farnoosh Heidarivincheh | D. Damen | M. Mirmehdi | Farnoosh Heidarivincheh
[1] Ruslan Salakhutdinov,et al. Action Recognition using Visual Attention , 2015, NIPS 2015.
[2] Andrew Zisserman,et al. Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.
[3] Xiaogang Wang,et al. Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Ling-Yu Duan,et al. Unified Spatio-Temporal Attention Networks for Action Recognition in Videos , 2019, IEEE Transactions on Multimedia.
[5] Cees Snoek,et al. VideoLSTM convolves, attends and flows for action recognition , 2016, Comput. Vis. Image Underst..
[6] Cordelia Schmid,et al. Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.
[7] Fernando De la Torre,et al. Max-Margin Early Event Detectors , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Luc Van Gool,et al. UntrimmedNets for Weakly Supervised Action Recognition and Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Tat-Seng Chua,et al. SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Amit K. Roy-Chowdhury,et al. W-TALC: Weakly-supervised Temporal Activity Localization and Classification , 2018, ECCV.
[11] Kate Saenko,et al. R-C3D: Region Convolutional 3D Network for Temporal Activity Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[12] 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).
[13] Louis-Philippe Morency,et al. Temporal Attention-Gated Model for Robust Sequence Classification , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Ivan Laptev,et al. On Space-Time Interest Points , 2005, International Journal of Computer Vision.
[15] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[16] Fei-Fei Li,et al. Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Misha Denil,et al. Learning Where to Attend with Deep Architectures for Image Tracking , 2011, Neural Computation.
[18] Jonathan Tompson,et al. Temporal Cycle-Consistency Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Li Fei-Fei,et al. Recurrent Attention Models for Depth-Based Person Identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Stan Sclaroff,et al. Learning Activity Progression in LSTMs for Activity Detection and Early Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Abhinav Gupta,et al. ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Majid Mirmehdi,et al. Beyond Action Recognition: Action Completion in RGB-D Data , 2016, BMVC.
[23] 乔宇. Motionlets: Mid-Level 3D Parts for Human Motion Recognition , 2013 .
[24] Dima Damen,et al. Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[25] Yu Qiao,et al. Recurrent Spatial-Temporal Attention Network for Action Recognition in Videos , 2018, IEEE Transactions on Image Processing.
[26] Shih-Fu Chang,et al. Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Wenjun Zeng,et al. An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data , 2016, AAAI.
[28] Ali Farhadi,et al. Much Ado About Time: Exhaustive Annotation of Temporal Data , 2016, HCOMP.
[29] Koray Kavukcuoglu,et al. Multiple Object Recognition with Visual Attention , 2014, ICLR.
[30] Thomas Serre,et al. HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.
[31] Majid Mirmehdi,et al. Action Completion: A Temporal Model for Moment Detection , 2018, BMVC.
[32] Rahul Sukthankar,et al. Rethinking the Faster R-CNN Architecture for Temporal Action Localization , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[33] Bohyung Han,et al. Weakly Supervised Action Localization by Sparse Temporal Pooling Network , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[34] Li Fei-Fei,et al. End-to-End Learning of Action Detection from Frame Glimpses in Videos , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Christopher Joseph Pal,et al. Describing Videos by Exploiting Temporal Structure , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[36] Dima Damen,et al. Action Recognition From Single Timestamp Supervision in Untrimmed Videos , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Baoxin Li,et al. Hierarchical Attention Network for Action Recognition in Videos , 2016, ArXiv.
[38] Alberto Del Bimbo,et al. Am I Done? Predicting Action Progress in Videos , 2017, ACM Trans. Multim. Comput. Commun. Appl..
[39] Mubarak Shah,et al. UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.
[40] Jing Xu,et al. Attention-Aware Compositional Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[41] Deva Ramanan,et al. Attentional Pooling for Action Recognition , 2017, NIPS.
[42] Li Fei-Fei,et al. Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos , 2015, International Journal of Computer Vision.