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Most modern approaches in temporal action localization divide this problem into two parts: (i) short-term feature extraction and (ii) long-range temporal boundary localization.
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. Advances in neural information processing systems 27
2014
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Caba Heilbron, F., Escorcia, V., Ghanem, B., Carlos Niebles, J.: Activitynet: A large-scale video benchmark for human activity understanding. In: Proceedings of the ieee conference on computer vision and pattern recognition. pp. 961–970 (2015)
2015
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Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 4489–4497 (2015)
2015
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2016
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
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2016
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Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: European conference on computer vision. pp. 646–661. Springer (2016)
2016
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Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Gool, L.V.: Temporal segment networks: Towards good practices for deep action recognition. In: European conference on computer vision. pp. 20–36. Springer (2016)
2016
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2016
Earlier work this paper cites.
Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6299–6308 (2017)
2017
Earlier work this paper cites.
Goyal, R., Ebrahimi Kahou, S., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al.: The" something something" video database for learning and evaluating visual common sense. In: Proceedings of the IEEE international conference on computer vision. pp. 5842–5850 (2017)
2017
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Idrees, H., Zamir, A.R., Jiang, Y.G., Gorban, A., Laptev, I., Sukthankar, R., Shah, M.: The thumos challenge on action recognition for videos “in the wild”. Computer Vision and Image Understanding 155
2017
Earlier work this paper cites.
Lin, T., Zhao, X., Shou, Z.: Single shot temporal action detection. In: Proceedings of the 25th ACM international conference on Multimedia. pp. 988–996 (2017)
2017
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3d residual networks. In: proceedings of the IEEE International Conference on Computer Vision. pp. 5533–5541 (2017)
2017
Earlier work this paper cites.
Shou, Z., Chan, J., Zareian, A., Miyazawa, K., Chang, S.F.: Cdc: Convolutional-de-convolutional networks for precise temporal action localization in untrimmed videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5734–5743 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Chao, Y.W., Vijayanarasimhan, S., Seybold, B., Ross, D.A., Deng, J., Sukthankar, R.: Rethinking the faster r-cnn architecture for temporal action localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1130–1139 (2018)
2018
Earlier work this paper cites.
Gao, J., Chen, K., Nevatia, R.: Ctap: Complementary temporal action proposal generation. In: Proceedings of the European conference on computer vision (ECCV). pp. 68–83 (2018)
2018
Earlier work this paper cites.
Lin, T., Zhao, X., Su, H., Wang, C., Yang, M.: Bsn: Boundary sensitive network for temporal action proposal generation. In: Proceedings of the European conference on computer vision (ECCV ). pp. 3–19 (2018)
2018
Cited alongside, same era.
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 6450–6459 (2018)
2018
Cited alongside, same era.
Wang, L., Li, W., Li, W., Van Gool, L.: Appearance-and-relation networks for video classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1430–1439 (2018)
2018
Cited alongside, same era.
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Van Gool, L.: Temporal segment networks for action recognition in videos. IEEE transactions on pattern analysis and machine intelligence 41
2018
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
Wang, X., Gao, C., Zhang, S., Sang, N.: Multi-level temporal pyramid network for action detection. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV). pp. 41–54. Springer (2020)
2020
Later among the works it cites.
Xu, M., Zhao, C., Rojas, D.S., Thabet, A., Ghanem, B.: G-tad: Sub-graph localization for temporal action detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10156–10165 (2020)
2020
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2018
Cited alongside, same era.
Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6202–6211 (2019)
2019
Cited alongside, same era.
Jiang, B., Wang, M., Gan, W., Wu, W., Yan, J.: Stm: Spatiotemporal and motion encoding for action recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2000–2009 (2019)
2019
Cited alongside, same era.
Lin, J., Gan, C., Han, S.: Tsm: Temporal shift module for efficient video understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7083–7093 (2019)
2019
Cited alongside, same era.
Lin, T., Liu, X., Li, X., Ding, E., Wen, S.: Bmn: Boundary-matching network for temporal action proposal generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3889–3898 (2019)
2019
Cited alongside, same era.
Liu, Y., Ma, L., Zhang, Y., Liu, W., Chang, S.F.: Multi-granularity generator for temporal action proposal. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3604–3613 (2019)
2019
Cited alongside, same era.
Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., Mei, T.: Gaussian temporal awareness networks for action localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 344–353 (2019)
2019
Cited alongside, same era.
Wu, C.Y., Feichtenhofer, C., Fan, H., He, K., Krahenbuhl, P., Girshick, R.: Long-term feature banks for detailed video understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 284–293 (2019)
2019
Cited alongside, same era.
Zhao, P., Xie, L., Ju, C., Zhang, Y., Wang, Y., Tian, Q.: Bottom-up temporal action localization with mutual regularization. In: European Conference on Computer Vision. pp. 539–555. Springer (2020)
2020
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., Feichtenhofer, C.: Multiscale vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6824–6835 (2021)
2021
Later among the works it cites.
Lin, C., Xu, C., Luo, D., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Fu, Y.: Learning salient boundary feature for anchor-free temporal action localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3320–3329 (2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10012–10022 (2021)
2021
Later among the works it cites.
2021
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Patrick, M., Campbell, D., Asano, Y., Misra, I., Metze, F., Feichtenhofer, C., Vedaldi, A., Henriques, J.F.: Keeping your eye on the ball: Trajectory attention in video transformers. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
Qing, Z., Su, H., Gan, W., Wang, D., Wu, W., Wang, X., Qiao, Y., Yan, J., Gao, C., Sang, N.: Temporal context aggregation network for temporal action proposal refinement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 485–494 (2021)
2021
Later among the works it cites.
Tan, J., Tang, J., Wang, L., Wu, G.: Relaxed transformer decoders for direct action proposal generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 13526–13535 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Xu, M., Xiong, Y., Chen, H., Li, X., Xia, W., Tu, Z., Soatto, S.: Long short-term transformer for online action detection. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
Zhang, C., Gupta, A., Zisserman, A.: Temporal query networks for fine-grained video understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4486–4496 (2021)
2021
Later among the works it cites.
Zhao, C., Thabet, A.K., Ghanem, B.: Video self-stitching graph network for temporal action localization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 13658–13667 (2021)
2021
Later among the works it cites.
Cheng, F., Xu, M., Xiong, Y., Chen, H., Li, X., Li, W., Xia, W.: Stochastic backpropagation: A memory efficient strategy for training video models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8301–8310 (2022)
2022
Closest in time.
You, C., Han, L., Feng, A., Zhao, R., Tang, H., Fan, W.: Megan: Memory enhanced graph attention network for space-time video super-resolution. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1401–1411 (2022)
2022
Closest in time.