Abstract
The motion of a video contains two factors: magnitude and direction, but most of the existing video self-supervised methods ignored the motion direction information. In this paper, we propose a Video Motion Perception (VMP) self-supervised framework, simultaneously taking account of the above two key factors. Specifically, a Motion Direction Perception Module (MDPM) is applied to asking the network to predict the moving direction of the video objects by using two well-designed handcraft strategies. Additionally, we analyze the characteristic of video motion in natural scenes and propose the Motion Change Perception Module (MCPM) accordingly for motion magnitude learning. Experimental results show that VMP achieves competitive performance on different benchmarks, including action recognition, video retrieval, and action similarity labeling.
Supported by the Beijing Municipal Science & Technology Commission (Z191100007119002), the Key Research Program of Frontier Sciences, CAS, Grant NO ZDBS-LY-7024.
W. Li and D. Luo—Equal contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Benaim, S., et al.: SpeedNet: learning the speediness in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9922–9931 (2020)
Chen, P., et al.: RSPNet: relative speed perception for unsupervised video representation learning. In: AAAI. vol. 1, p. 5 (2021)
Cho, H., Kim, T., Chang, H.J., Hwang, W.: Self-supervised spatio-temporal representation learning using variable playback speed prediction, vol. 2, pp. 13–14. arXiv preprint arXiv:2003.02692 (2020)
Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015)
Han, T., Xie, W., Zisserman, A.: Self-supervised co-training for video representation learning. NeurIPS 33, 5679–5690 (2020)
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)
Jenni, S., Meishvili, G., Favaro, P.: Video representation learning by recognizing temporal transformations. In: Proceedings of the European Conference on Computer Vision, pp. 425–442 (2020)
Kay, W., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)
Kim, D., Cho, D., Kweon, I.S.: Self-supervised video representation learning with space-time cubic puzzles. In: AAAI, vol. 33, pp. 8545–8552 (2019)
Kliper-Gross, O., Hassner, T., et al.: The action similarity labeling challenge. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 615–621 (2011)
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2556–2563. IEEE (2011)
Luo, D., et al.: Video cloze procedure for self-supervised spatio-temporal learning. In: AAAI, pp. 11701–11708 (2020)
Luo, D., Zhou, Y., Fang, B., Zhou, Y., Wu, D., Wang, W.: Exploring relations in untrimmed videos for self-supervised learning. ACM Trans. Multimed. Comput. Commun. App. (TOMM) 18(1s), 1–21 (2022)
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69–84. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_5
Pan, T., et al.: VideoMoCo: contrastive video representation learning with temporally adversarial examples. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11205–11214 (2021)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NeurIPS, pp. 568–576 (2014)
Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)
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)
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)
Wang, J., Jiao, J., Liu, Y.-H.: Self-supervised video representation learning by pace prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 504–521. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58520-4_30
Wang, J., et al.: Self-supervised spatio-temporal representation learning for videos by predicting motion and appearance statistics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4006–4015 (2019)
Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_2
Wei, D., Lim, J.J., Zisserman, A., Freeman, W.T.: Learning and using the arrow of time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8052–8060 (2018)
Xie, S., Sun, C., Huang, J., Tu, Z., Murphy, K.: Rethinking spatiotemporal feature learning: speed-accuracy trade-offs in video classification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 318–335. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_19
Xu, D., Xiao, J., Zhao, Z., Shao, J., Xie, D., Zhuang, Y.: Self-supervised spatiotemporal learning via video clip order prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10334–10343 (2019)
Yao, Y., Liu, C., Luo, D., Zhou, Y., Ye, Q.: Video playback rate perception for self-supervised spatio-temporal representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6548–6557 (2020)
Zhou, B., Andonian, A., Oliva, A., Torralba, A.: Temporal relational reasoning in videos. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 831–846. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_49
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, W., Luo, D., Fang, B., Li, X., Zhou, Y., Wang, W. (2022). Video Motion Perception for Self-supervised Representation Learning. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-15937-4_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15936-7
Online ISBN: 978-3-031-15937-4
eBook Packages: Computer ScienceComputer Science (R0)