Advertisement

AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12353)

Abstract

Convolutional operations have two limitations: (1) do not explicitly model where to focus as the same filter is applied to all the positions, and (2) are unsuitable for modeling long-range dependencies as they only operate on a small neighborhood. While both limitations can be alleviated by attention operations, many design choices remain to be determined to use attention, especially when applying attention to videos. Towards a principled way of applying attention to videos, we address the task of spatiotemporal attention cell search. We propose a novel search space for spatiotemporal attention cells, which allows the search algorithm to flexibly explore various design choices in the cell. The discovered attention cells can be seamlessly inserted into existing backbone networks, e.g., I3D or S3D, and improve video classification accuracy by more than 2% on both Kinetics-600 and MiT datasets. The discovered attention cells outperform non-local blocks on both datasets, and demonstrate strong generalization across different modalities, backbones, and datasets. Inserting our attention cells into I3D-R50 yields state-of-the-art performance on both datasets.

Keywords

Attention Video classification Neural architecture search 

Notes

Acknowledgement

We thank Guanhang Wu and Yinxiao Li for insightful discussions and the larger Google Cloud Video AI team for the support.

Supplementary material

504445_1_En_27_MOESM1_ESM.pdf (264 kb)
Supplementary material 1 (pdf 263 KB)

References

  1. 1.
    Baker, B., Gupta, O., Naik, N., Raskar, R.: Designing neural network architectures using reinforcement learning. In: ICLR (2017)Google Scholar
  2. 2.
    Bello, I., Zoph, B., Vaswani, A., Shlens, J., Le, Q.V.: Attention augmented convolutional networks. In: ICCV (2019)Google Scholar
  3. 3.
    Cao, S., Wang, X., Kitani, K.M.: Learnable embedding space for efficient neural architecture compression. In: ICLR (2019)Google Scholar
  4. 4.
    Carreira, J., Noland, E., Banki-Horvath, A., Hillier, C., Zisserman, A.: A short note about kinetics-600. arXiv preprint arXiv:1808.01340 (2018)
  5. 5.
    Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: CVPR (2017)Google Scholar
  6. 6.
    Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description. In: CVPR (2015)Google Scholar
  7. 7.
    Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: ICCV (2019)Google Scholar
  8. 8.
    Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: CVPR (2016)Google Scholar
  9. 9.
    He, D., et al.: StNET: local and global spatial-temporal modeling for action recognition. In: AAAI (2019)Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  11. 11.
    Kandasamy, K., Neiswanger, W., Schneider, J., Poczos, B., Xing, E.P.: Neural architecture search with Bayesian optimisation and optimal transport. In: NeurIPS (2018)Google Scholar
  12. 12.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NeurIPS (2012)Google Scholar
  13. 13.
    Li, L., Talwalkar, A.: Random search and reproducibility for neural architecture search. In: UAI (2019)Google Scholar
  14. 14.
    Liu, C., et al.: Auto-deeplab: hierarchical neural architecture search for semantic image segmentation. In: CVPR (2019)Google Scholar
  15. 15.
    Liu, C.: Progressive neural architecture search. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 19–35. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01246-5_2CrossRefGoogle Scholar
  16. 16.
    Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: ICLR (2019)Google Scholar
  17. 17.
    Liu, X., Lee, J.Y., Jin, H.: Learning video representations from correspondence proposals. In: CVPR (2019)Google Scholar
  18. 18.
    Monfort, M., et al.: Moments in time dataset: one million videos for event understanding. TPAMI 42, 502–508 (2019)CrossRefGoogle Scholar
  19. 19.
    Park, J., Woo, S., Lee, J.Y., Kweon, I.S.: Bam: bottleneck attention module. In: BMVC (2018)Google Scholar
  20. 20.
    Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3D residual networks. In: ICCV (2017)Google Scholar
  21. 21.
    Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: AAAI (2019)Google Scholar
  22. 22.
    Real, E., et al.: Large-scale evolution of image classifiers. In: ICML (2017)Google Scholar
  23. 23.
    Ryoo, M.S., Piergiovanni, A., Tan, M., Angelova, A.: Assemblenet: searching for multi-stream neural connectivity in video architectures. In: ICLR (2020)Google Scholar
  24. 24.
    Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NeurIPS (2014)Google Scholar
  25. 25.
    Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: NeurIPS (2012)Google Scholar
  26. 26.
    Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.W.: Gaussian process optimization in the bandit setting: no regret and experimental design. In: ICML (2009)Google Scholar
  27. 27.
    Stroud, J., Ross, D., Sun, C., Deng, J., Sukthankar, R.: D3d: Distilled 3D networks for video action recognition. In: WACV (2020)Google Scholar
  28. 28.
    Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)Google Scholar
  29. 29.
    Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: ICCV (2015)Google Scholar
  30. 30.
    Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)Google Scholar
  31. 31.
    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_2CrossRefGoogle Scholar
  32. 32.
    Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR (2018)Google Scholar
  33. 33.
    Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01234-2_1CrossRefGoogle Scholar
  34. 34.
    Xie, L., Yuille, A.: Genetic CNN. In: ICCV (2017)Google Scholar
  35. 35.
    Xie, S., Kirillov, A., Girshick, R., He, K.: Exploring randomly wired neural networks for image recognition. In: ICCV (2019)Google Scholar
  36. 36.
    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_19CrossRefGoogle Scholar
  37. 37.
    Yu, K., Sciuto, C., Jaggi, M., Musat, C., Salzmann, M.: Evaluating the search phase of neural architecture search. In: ICLR (2020)Google Scholar
  38. 38.
    Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: Deep networks for video classification. In: CVPR (2015)Google Scholar
  39. 39.
    Zhong, Z., Yan, J., Wu, W., Shao, J., Liu, C.L.: Practical block-wise neural network architecture generation. In: CVPR (2018)Google Scholar
  40. 40.
    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_49CrossRefGoogle Scholar
  41. 41.
    Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: ICLR (2017)Google Scholar
  42. 42.
    Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: CVPR (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.GoogleMountain ViewUSA
  2. 2.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations