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Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content.
Fast temporal activity proposals for efficient detection of human actions in untrimmed videos
Caba Heilbron, F., Carlos Niebles, J., Ghanem, B.: · 1923
Earlier work this paper cites.
Convolutional two-stream network fusion for video action recognition
Feichtenhofer, C., Pinz, A., Zisserman, A.: · 1941
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: · 2010
Earlier work this paper cites.
Action recognition by dense trajectories
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: · 2011
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A.R., Shah, M.: · 2012
Earlier work this paper cites.
Action recognition with improved trajectories
Wang, H., Schmid, C.: · 2013
Earlier work this paper cites.
Selective search for object recognition
Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: · 2013
Earlier work this paper cites.
Thumos challenge: Action recognition with a large number of classes
Jiang, Y.G., Liu, J., Zamir, A.R., Toderici, G., Laptev, I., Shah, M., Sukthankar, R.: · 2014
Earlier work this paper cites.
Two-stream convolutional networks for action recognition in videos
Simonyan, K., Zisserman, A.: · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
Earlier work this paper cites.
Edge boxes: Locating object proposals from edges
Zitnick, C.L., Dollár, P.: · 2014
Earlier work this paper cites.
Fast saliency based pooling of fisher encoded dense trajectories
Karaman, S., Seidenari, L., Del Bimbo, A.: · 2014
Earlier work this paper cites.
The lear submission at thumos 2014
Oneata, D., Verbeek, J., Schmid, C.: · 2014
Earlier work this paper cites.
Action recognition and detection by combining motion and appearance features
Wang, L., Qiao, Y., Tang, X.: · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: · 2014
Earlier work this paper cites.
Activitynet: A large-scale video benchmark for human activity understanding
Caba Heilbron, F., Escorcia, V., Ghanem, B., Carlos Niebles, J.: · 2015
Earlier work this paper cites.
Learning spatiotemporal features with 3d convolutional networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: · 2015
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Towards good practices for very deep two-stream convnets
Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: · 2015
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Fast r-cnn
Girshick, R.: · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Kuo, W., Hariharan, B., Malik, J.: · 2015
Cited alongside, same era.
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Ioffe, S., Szegedy, C.: · 2015
Turn tap: Temporal unit regression network for temporal action proposals
Gao, J., Yang, Z., Sun, C., Chen, K., Nevatia, R.: · 2017
Later among the works it cites.
Temporal action detection with structured segment networks
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Lin, D., Tang, X.: · 2017
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Single shot temporal action detection
Lin, T., Zhao, X., Shou, Z.: · 2017
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End-to-end, single-stream temporal action detection in untrimmed videos
Buch, S., Escorcia, V., Ghanem, B., Fei-Fei, L., Niebles, J.C.: · 2017
Later among the works it cites.
Temporal action localization by structured maximal sums
Yuan, Z., Stroud, J.C., Lu, T., Deng, J.: · 2017
Later among the works it cites.
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Cited alongside, same era.
Daps: Deep action proposals for action understanding
Escorcia, V., Heilbron, F.C., Niebles, J.C., Ghanem, B.: · 2016
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Temporal action localization in untrimmed videos via multi-stage cnns
Shou, Z., Wang, D., Chang, S.F.: · 2016
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Feature pyramid networks for object detection
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: · 2016
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Locnet: Improving localization accuracy for object detection
Gidaris, S., Komodakis, N.: · 2016
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Untrimmed video classification for activity detection: submission to activitynet challenge
Singh, G., Cuzzolin, F.: · 2016
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Temporal segment networks: towards good practices for deep action recognition
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Van Gool, L.: · 2016
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Cascaded boundary regression for temporal action detection
Gao, J., Yang, Z., Nevatia, R.: · 2017
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Improving object detection with one line of code
Bodla, N., Singh, B., Chellappa, R., Davis, .L.S.: · 2017
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Temporal convolution based action proposal: Submission to activitynet 2017
Lin, T., Zhao, X., Shou, Z.: · 2017
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Temporal context network for activity localization in videos
Dai, X., Singh, B., Zhang, G., Davis, L.S., Chen, Y.Q.: · 2017
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Activitynet challenge 2017 summary
Ghanem, B., Niebles, J.C., Snoek, C., Heilbron, F.C., Alwassel, H., Khrisna, R., Escorcia, V., Hata, K., Buch, S.: · 2017
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Convnet architecture search for spatiotemporal feature learning
Tran, D., Ray, J., Shou, Z., Chang, S.F., Paluri, M.: · 2017
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Cuhk & ethz & siat submission to activitynet challenge 2017
Zhao, Y., Zhang, B., Wu, Z., Yang, S., Zhou, L., Yan, S., Wang, L., Xiong, Y., Lin, D., Qiao, Y., Tang, X.: · 2017
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Untrimmednets for weakly supervised action recognition and detection
Wang, L., Xiong, Y., Lin, D., Van Gool, L.: · 2017
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Scc: Semantic context cascade for efficient action detection
Heilbron, F.C., Barrios, W., Escorcia, V., Ghanem, B.: · 2017
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Shou, Z., Chan, J., Zareian, A., Miyazawa, K., Chang, S.F.: · 2017
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A pursuit of temporal accuracy in general activity detection
Xiong, Y., Zhao, Y., Wang, L., Lin, D., Tang, X.: · 2017
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R-c3d: Region convolutional 3d network for temporal activity detection
Xu, H., Das, A., Saenko, K.: · 2017
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