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Self-supervised Learning of Audio-Visual Objects from Video

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12363)

Abstract

Our objective is to transform a video into a set of discrete audio-visual objects using self-supervised learning. To this end, we introduce a model that uses attention to localize and group sound sources, and optical flow to aggregate information over time. We demonstrate the effectiveness of the audio-visual object embeddings that our model learns by using them for four downstream speech-oriented tasks: (a) multi-speaker sound source separation, (b) localizing and tracking speakers, (c) correcting misaligned audio-visual data, and (d) active speaker detection. Using our representation, these tasks can be solved entirely by training on unlabeled video, without the aid of object detectors. We also demonstrate the generality of our method by applying it to non-human speakers, including cartoons and puppets. Our model significantly outperforms other self-supervised approaches, and obtains performance competitive with methods that use supervised face detection.

Notes

Acknowledgements

We thank V. Kalogeiton for generous help with the annotations and the Friends videos, A. A. Efros for helpful discussions, L. Momeni, T. Han and Q. Pleple for proofreading, A. Dutta for help with VIA, and A. Thandavan for infrastructure support. This work is funded by the UK EPSRC CDT in AIMS, DARPA Medifor, and a Google-DeepMind Graduate Scholarship.

Supplementary material

504473_1_En_13_MOESM1_ESM.zip (38 mb)
Supplementary material 1 (zip 38895 KB)

References

  1. 1.
    Afouras, T., Chung, J.S., Senior, A., Vinyals, O., Zisserman, A.: Deep audio-visual speech recognition. IEEE PAMI (2019)Google Scholar
  2. 2.
    Afouras, T., Chung, J.S., Zisserman, A.: The conversation: deep audio-visual speech enhancement. In: INTERSPEECH (2018)Google Scholar
  3. 3.
    Afouras, T., Chung, J.S., Zisserman, A.: LRS3-TED: a large-scale dataset for visual speech recognition. In: arXiv preprint arXiv:1809.00496 (2018)
  4. 4.
    Afouras, T., Chung, J.S., Zisserman, A.: My lips are concealed: audio-visual speech enhancement through obstructions. In: INTERSPEECH (2019)Google Scholar
  5. 5.
    Arandjelović, R., Zisserman, A.: Look, listen and learn. In: Proceedings of ICCV (2017)Google Scholar
  6. 6.
    Arandjelović, R., Zisserman, A.: Objects that sound. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 451–466. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01246-5_27CrossRefGoogle Scholar
  7. 7.
    Barzelay, Z., Schechner, Y.Y.: Harmony in motion. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  8. 8.
    Chakravarty, P., Tuytelaars, T.: Cross-modal supervision for learning active speaker detection in video. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 285–301. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46454-1_18CrossRefGoogle Scholar
  9. 9.
    Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 (2014)
  10. 10.
    Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. ICML (2020)Google Scholar
  11. 11.
    Chung, J.S., Lee, B.J., Han, I.: Who said that?: Audio-visual speaker diarisation of real-world meetings. In: Interspeech (2019)Google Scholar
  12. 12.
    Chung, J.S., Nagrani, A., Zisserman, A.: VoxCeleb2: deep speaker recognition. In: INTERSPEECH (2018)Google Scholar
  13. 13.
    Chung, J.S., Zisserman, A.: Out of time: automated lip sync in the wild. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016. LNCS, vol. 10117, pp. 251–263. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-54427-4_19CrossRefGoogle Scholar
  14. 14.
    Chung, J.S., Zisserman, A.: Signs in time: encoding human motion as a temporal image. In: Workshop on Brave New Ideas for Motion Representations, ECCV (2016)Google Scholar
  15. 15.
    Chung, S.W., Chung, J.S., Kang, H.G.: Perfect match: improved cross-modal embeddings for audio-visual synchronisation. In: Proceedings of ICASSP, pp. 3965–3969. IEEE (2019)Google Scholar
  16. 16.
    Cutler, R., Davis, L.: Look who’s talking: speaker detection using video and audio correlation. In: 2000 IEEE International Conference on Multimedia and Expo. ICME 2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No. 00TH8532), vol. 3, pp. 1589–1592. IEEE (2000)Google Scholar
  17. 17.
    Deng, J., Guo, J., Yuxiang, Z., Yu, J., Kotsia, I., Zafeiriou, S.: Retinaface: Single-stage dense face localisation in the wild. In: arxiv (2019)Google Scholar
  18. 18.
    Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of ICCV, pp. 1422–1430 (2015)Google Scholar
  19. 19.
    Dutta, A., Zisserman, A.: The VIA annotation software for images, audio and video. In: Proceedings of the 27th ACM International Conference on Multimedia. MM 2019. ACM, New York (2019)Google Scholar
  20. 20.
    Ephrat, A., et al.: Looking to listen at the cocktail party: a speaker-independent audio-visual model for speech separation. ACM Trans. Graph. (TOG) 37(4), 112 (2018)CrossRefGoogle Scholar
  21. 21.
    Févotte, C., Gribonval, R., Vincent, E.: BSS EVAL toolbox user guide. IRISA Technical Report 1706 (2005). http://www.irisa.fr/metiss/bsseval/
  22. 22.
    Fisher III, J.W., Darrell, T., Freeman, W.T., Viola, P.A.: Learning joint statistical models for audio-visual fusion and segregation. In: NeurIPS (2000)Google Scholar
  23. 23.
    Gabbay, A., Ephrat, A., Halperin, T., Peleg, S.: Seeing through noise: visually driven speaker separation and enhancement. In: Proceedings of ICASSP, pp. 3051–3055. IEEE (2018)Google Scholar
  24. 24.
    Gadde, R., Jampani, V., Gehler, P.V.: Semantic video CNNs through representation warping. In: Proceedings of ICCV, pp. 4463–4472 (2017)Google Scholar
  25. 25.
    Gan, C., Zhao, H., Chen, P., Cox, D., Torralba, A.: Self-supervised moving vehicle tracking with stereo sound. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 7053–7062 (2019)Google Scholar
  26. 26.
    Gao, R., Feris, R., Grauman, K.: Learning to separate object sounds by watching unlabeled video. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 36–54. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01219-9_3CrossRefGoogle Scholar
  27. 27.
    Gao, R., Grauman, K.: 2.5D visual sound. In: CVPR (2019)Google Scholar
  28. 28.
    Gao, R., Grauman, K.: Co-separating sounds of visual objects. arXiv preprint arXiv:1904.07750 (2019)
  29. 29.
    Han, T., Xie, W., Zisserman, A.: Video representation learning by dense predictive coding. In: Workshop on Large Scale Holistic Video Understanding, ICCV (2019)Google Scholar
  30. 30.
    Han, T., Xie, W., Zisserman, A.: Memory-augmented dense predictive coding for video representation learning. In: ECCV (2020)Google Scholar
  31. 31.
    Harwath, D., Recasens, A., Surís, D., Chuang, G., Torralba, A., Glass, J.: Jointly discovering visual objects and spoken words from raw sensory input. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 659–677. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01231-1_40CrossRefGoogle Scholar
  32. 32.
    He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)Google Scholar
  33. 33.
    Hénaff, O.J., et al.: Data-efficient image recognition with contrastive predictive coding. In: ICML (2020)Google Scholar
  34. 34.
    Hershey, J., Movellan, J.: Audio-vision: locating sounds via audio-visual synchrony. In: NeurIPS, vol. 12 (1999)Google Scholar
  35. 35.
    Hu, D., Nie, F., Li, X.: Deep multimodal clustering for unsupervised audiovisual learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019Google Scholar
  36. 36.
    Hu, D., Wang, Z., Xiong, H., Wang, D., Nie, F., Dou, D.: Curriculum audiovisual learning. arXiv preprint arXiv:2001.09414 (2020)
  37. 37.
    Izadinia, H., Saleemi, I., Shah, M.: Multimodal analysis for identification and segmentation of moving-sounding objects. IEEE Trans. Multimedia 15(2), 378–390 (2012)CrossRefGoogle Scholar
  38. 38.
    Khosravan, N., Ardeshir, S., Puri, R.: On attention modules for audio-visual synchronization. arXiv preprint arXiv:1812.06071 (2018)
  39. 39.
    Kidron, E., Schechner, Y.Y., Elad, M.: Pixels that sound. In: Proceedings of CVPR (2005)Google Scholar
  40. 40.
    Korbar, B., Tran, D., Torresani, L.: Co-training of audio and video representations from self-supervised temporal synchronization. CoRR (2018)Google Scholar
  41. 41.
    Misra, I., van der Maaten, L.: Self-supervised learning of pretext-invariant representations. In: CVPR (2020)Google Scholar
  42. 42.
    Nagrani, A., Chung, J.S., Albanie, S., Zisserman, A.: Disentangled speech embeddings using cross-modal self-supervision. In: Proceedings of ICASSP, pp. 6829–6833. IEEE (2020)Google Scholar
  43. 43.
    Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
  44. 44.
    Owens, A., Efros, A.A.: Audio-visual scene analysis with self-supervised multisensory features. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 639–658. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01231-1_39CrossRefGoogle Scholar
  45. 45.
    Owens, A., Isola, P., McDermott, J., Torralba, A., Adelson, E.H., Freeman, W.T.: Visually indicated sounds. In: Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  46. 46.
    Owens, A., Wu, J., McDermott, J.H., Freeman, W.T., Torralba, A.: Learning sight from sound: ambient sound provides supervision for visual learning. Int. J. Comput. Vis. (2018)Google Scholar
  47. 47.
    Pfister, T., Charles, J., Zisserman, A.: Flowing convnets for human pose estimation in videos. In: Proceedings of ICCV (2015)Google Scholar
  48. 48.
    Ramaswamy, J., Das, S.: See the sound, hear the pixels. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), March 2020Google Scholar
  49. 49.
    Rix, A.W., Beerends, J.G., Hollier, M.P., Hekstra, A.P.: Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. In: Proceedings of ICASSP, vol. 2, pp. 749–752. IEEE (2001)Google Scholar
  50. 50.
    Roth, J., et al.: AVA-ActiveSpeaker: An audio-visual dataset for active speaker detection. arXiv preprint arXiv:1901.01342 (2019)
  51. 51.
    Rouditchenko, A., Zhao, H., Gan, C., McDermott, J., Torralba, A.: Self-supervised audio-visual co-segmentation. In: Proceedings of ICASSP, pp. 2357–2361. IEEE (2019)Google Scholar
  52. 52.
    Senocak, A., Oh, T.H., Kim, J., Yang, M.H., Kweon, I.S.: Learning to localize sound source in visual scenes. In: Proceedings of CVPR (2018)Google Scholar
  53. 53.
    Shahid, M., Beyan, C., Murino, V.: Voice activity detection by upper body motion analysis and unsupervised domain adaptation. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, October 2019Google Scholar
  54. 54.
    Tian, Y., Shi, J., Li, B., Duan, Z., Xu, C.: Audio-visual event localization in unconstrained videos. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 252–268. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01216-8_16CrossRefGoogle Scholar
  55. 55.
    Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. arXiv preprint arXiv:1906.05849 (2019)
  56. 56.
    Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: Proceedings of ICCV, pp. 2794–2802 (2015)Google Scholar
  57. 57.
    Zhao, H., Gan, C., Ma, W.C., Torralba, A.: The sound of motions. In: Proceedings of ICCV (2019)Google Scholar
  58. 58.
    Zhao, H., Gan, C., Rouditchenko, A., Vondrick, C., McDermott, J., Torralba, A.: The sound of pixels. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 587–604. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01246-5_35CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of OxfordOxfordUK
  2. 2.University of MichiganAnn ArborUSA
  3. 3.Naver CorporationSeongnam-siSouth Korea

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