Neural machine translation by jointly learning to align and translate, 2015. ,
Curriculum learning, Proceedings of the 26th annual international conference on machine learning, pp.41-48, 2009. ,
Weakly supervised deep detection networks, CVPR, 2016. ,
Quo vadis, action recognition? a new model and the kinetics dataset, CVPR, 2017. ,
Weakly supervised object localization with multi-fold multiple instance learning, IEEE transactions on pattern analysis and machine intelligence, vol.39, pp.189-203, 2017. ,
Attentional pooling for action recognition, Advances in Neural Information Processing Systems, pp.33-44, 2017. ,
Contextual action recognition with r* cnn, Proceedings of the IEEE international conference on computer vision, pp.1080-1088, 2015. ,
DOI : 10.1109/iccv.2015.129
URL : http://arxiv.org/pdf/1505.01197
Inductive representation learning on large graphs, Advances in Neural Information Processing Systems, pp.1025-1035, 2017. ,
Activitynet: A large-scale video benchmark for human activity understanding, CVPR, 2015. ,
DOI : 10.1109/cvpr.2015.7298698
URL : https://repository.kaust.edu.sa/bitstream/10754/556141/1/ActivityNet_CVPR2015.pdf
Teaching machines to read and comprehend, Advances in Neural Information Processing Systems, pp.1693-1701, 2015. ,
, THUMOS challenge: Action recognition with a large number of classes, 2014.
ContextLocNet: Context-aware deep network models for weakly supervised localization, ECCV, 2016. ,
DOI : 10.1007/978-3-319-46454-1_22
URL : https://hal.archives-ouvertes.fr/hal-01421772
The kinetics human action video dataset, 2017. ,
, Structured attention networks, 2017.
Adam: A method for stochastic optimization, 2014. ,
Low-rank bilinear pooling for fine-grained classification, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.7025-7034, 2017. ,
DOI : 10.1109/cvpr.2017.743
URL : http://arxiv.org/pdf/1611.05109
Differentiable dynamic programming for structured prediction and attention, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01809550
Weakly supervised action localization by sparse temporal pooling network, 2018. ,
DOI : 10.1109/cvpr.2018.00706
URL : http://arxiv.org/pdf/1712.05080
Is object localization for free?-weaklysupervised learning with convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.685-694, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01015140
From image-level to pixel-level labeling with convolutional networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1713-1721, 2015. ,
Temporal action detection using a statistical language model, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3131-3140, 2016. ,
DOI : 10.1109/cvpr.2016.341
Imagenet large scale visual recognition challenge, International Journal of Computer Vision, vol.115, issue.3, pp.211-252, 2015. ,
DOI : 10.1007/s11263-015-0816-y
URL : http://arxiv.org/pdf/1409.0575
Action recognition using visual attention, 2015. ,
Temporal action localization in untrimmed videos via multi-stage cnns, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1049-1058, 2016. ,
CDC: convolutional-de-convolutional networks for precise temporal action localization in untrimmed videos, CVPR, 2017. ,
Autoloc: Weaklysupervised temporal action localization in untrimmed videos, Proceedings of the European Conference on Computer Vision (ECCV), pp.154-171, 2018. ,
Two-stream convolutional networks for action recognition in videos, Advances in neural information processing systems, pp.568-576, 2014. ,
Untrimmed video classification for activity detection: submission to activitynet challenge, 2016. ,
Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization, The IEEE International Conference on Computer Vision (ICCV, 2017. ,
Learning spatiotemporal features with 3d convolutional networks, Proceedings of the IEEE international conference on computer vision, pp.4489-4497, 2015. ,
Attention is all you need, Advances in Neural Information Processing Systems, pp.6000-6010, 2017. ,
The Caltech-UCSD Birds-200-2011 Dataset, 2011. ,
Temporal segment networks: Towards good practices for deep action recognition, European Conference on Computer Vision, pp.20-36, 2016. ,
Untrimmednets for weakly supervised action recognition and detection, 2017. ,
Acitivitynet large scale activity recognition challenge, UTS at Activitynet, 2016. ,
Object region mining with adversarial erasing: A simple classification to semantic segmentation approach, IEEE CVPR, 2017. ,
A pursuit of temporal accuracy in general activity detection, 2017. ,
R-c3d: Region convolutional 3d network for temporal activity detection, 2017. ,
End-to-end learning of action detection from frame glimpses in videos, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2678-2687, 2016. ,
Temporal action localization with pyramid of score distribution features, CVPR, 2016. ,
Temporal dynamic graph LSTM for action-driven video object detection, ICCV, pp.1819-1828, 2017. ,
Temporal action localization by structured maximal sums, CVPR, 2017. ,
Co-saliency detection via a self-paced multipleinstance learning framework, IEEE transactions on pattern analysis and machine intelligence, vol.39, pp.865-878, 2017. ,
, Gaan: Gated attention networks for learning on large and spatiotemporal graphs, 2018.
Adversarial complementary learning for weakly supervised object localization, 2018. ,
DOI : 10.1109/cvpr.2018.00144
URL : http://arxiv.org/pdf/1804.06962
Temporal action detection with structured segment networks, ICCV, 2017. ,
DOI : 10.1109/iccv.2017.317
URL : http://arxiv.org/pdf/1704.06228
, Learning Deep Features for Discriminative Localization. CVPR, 2016.
DOI : 10.1109/cvpr.2016.319
URL : http://arxiv.org/pdf/1512.04150
Object detectors emerge in deep scene cnns, 2014. ,
Learning deep features for discriminative localization, Computer Vision and Pattern Recognition, 2016. ,
DOI : 10.1109/cvpr.2016.319
URL : http://arxiv.org/pdf/1512.04150
Soft proposal networks for weakly supervised object localization, 2017. ,
DOI : 10.1109/iccv.2017.204
URL : http://arxiv.org/pdf/1709.01829