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Kernelized Memory Network for Video Object Segmentation

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

Semi-supervised video object segmentation (VOS) is a task that involves predicting a target object in a video when the ground truth segmentation mask of the target object is given in the first frame. Recently, space-time memory networks (STM) have received significant attention as a promising solution for semi-supervised VOS. However, an important point is overlooked when applying STM to VOS. The solution (STM) is non-local, but the problem (VOS) is predominantly local. To solve the mismatch between STM and VOS, we propose a kernelized memory network (KMN). Before being trained on real videos, our KMN is pre-trained on static images, as in previous works. Unlike in previous works, we use the Hide-and-Seek strategy in pre-training to obtain the best possible results in handling occlusions and segment boundary extraction. The proposed KMN surpasses the state-of-the-art on standard benchmarks by a significant margin (+5% on DAVIS 2017 test-dev set). In addition, the runtime of KMN is 0.12 s per frame on the DAVIS 2016 validation set, and the KMN rarely requires extra computation, when compared with STM.

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Notes

  1. 1.

    https://davischallenge.org/davis2017/soa_compare.html.

  2. 2.

    https://github.com/seoungwugoh/STM.

References

  1. Bao, L., Wu, B., Liu, W.: CNN in MRF: Video object segmentation via inference in a CNN-based higher-order spatio-temporal MRF. In: CVPR, pp. 5977–5986 (2018)

    Google Scholar 

  2. Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Van Gool, L.: One-shot video object segmentation. In: CVPR, pp. 221–230 (2017)

    Google Scholar 

  3. Chen, Y., Pont-Tuset, J., Montes, A., Van Gool, L.: Blazingly fast video object segmentation with pixel-wise metric learning. In: CVPR, pp. 1189–1198 (2018)

    Google Scholar 

  4. Cheng, J., Tsai, Y.H., Hung, W.C., Wang, S., Yang, M.H.: Fast and accurate online video object segmentation via tracking parts. In: CVPR, pp. 7415–7424 (2018)

    Google Scholar 

  5. Cheng, J., Tsai, Y.H., Wang, S., Yang, M.H.: SegFlow: joint learning for video object segmentation and optical flow. In: ICCV, pp. 686–695 (2017)

    Google Scholar 

  6. Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2014)

    Google Scholar 

  7. Ci, H., Wang, C., Wang, Y.: Video object segmentation by learning location-sensitive embeddings. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 524–539. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_31

    Chapter  Google Scholar 

  8. Duarte, K., Rawat, Y.S., Shah, M.: Capsulevos: semi-supervised video object segmentation using capsule routing. In: ICCV, October 2019

    Google Scholar 

  9. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Google Scholar 

  10. Girdhar, R., Carreira, J., Doersch, C., Zisserman, A.: Video action transformer network. In: CVPR, pp. 244–253 (2019)

    Google Scholar 

  11. Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: ICCV, pp. 991–998. IEEE (2011)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  14. Hu, Y.T., Huang, J.B., Schwing, A.: MaskRNN: instance level video object segmentation. In: NIPS, pp. 325–334 (2017)

    Google Scholar 

  15. Hu, Y.-T., Huang, J.-B., Schwing, A.G.: VideoMatch: matching based video object segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 56–73. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_4

    Chapter  Google Scholar 

  16. Jampani, V., Gadde, R., Gehler, P.V.: Video propagation networks. In: CVPR, pp. 451–461 (2017)

    Google Scholar 

  17. Johnander, J., Danelljan, M., Brissman, E., Khan, F.S., Felsberg, M.: A generative appearance model for end-to-end video object segmentation. In: CVPR, pp. 8953–8962 (2019)

    Google Scholar 

  18. Khoreva, A., Benenson, R., Ilg, E., Brox, T., Schiele, B.: Lucid data dreaming for video object segmentation. Int. J. Comput. Vis. 127(9), 1175–1197 (2019)

    Google Scholar 

  19. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

    Google Scholar 

  20. Kumar, A., et al.: Ask me anything: dynamic memory networks for natural language processing. In: ICML, pp. 1378–1387 (2016)

    Google Scholar 

  21. Lee, J., Kim, D., Ponce, J., Ham, B.: SFNet: learning object-aware semantic correspondence. In: CVPR, pp. 2278–2287 (2019)

    Google Scholar 

  22. Li, X., Loy, C.C.: Video object segmentation with joint re-identification and attention-aware mask propagation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 93–110. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_6

    Chapter  Google Scholar 

  23. Lin, H., Qi, X., Jia, J.: AGSS-VOS: attention guided single-shot video object segmentation. In: ICCV, October 2019

    Google Scholar 

  24. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  25. Luiten, J., Voigtlaender, P., Leibe, B.: PReMVOS: proposal-generation, refinement and merging for video object segmentation. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11364, pp. 565–580. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20870-7_35

    Chapter  Google Scholar 

  26. Maninis, K.K., et al.: Video object segmentation without temporal information. IEEE Trans. Pattern Anal. Mach. Intell. 41(6), 1515–1530 (2018)

    Google Scholar 

  27. Märki, N., Perazzi, F., Wang, O., Sorkine-Hornung, A.: Bilateral space video segmentation. In: CVPR, pp. 743–751 (2016)

    Google Scholar 

  28. Miller, A., Fisch, A., Dodge, J., Karimi, A.H., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents. In: EMNLP (2016)

    Google Scholar 

  29. Oh, S.W., Lee, J.Y., Sunkavalli, K., Joo Kim, S.: Fast video object segmentation by reference-guided mask propagation. In: CVPR, pp. 7376–7385 (2018)

    Google Scholar 

  30. Oh, S.W., Lee, J.Y., Xu, N., Kim, S.J.: Video object segmentation using space-time memory networks. In: ICCV, October 2019

    Google Scholar 

  31. Parmar, N., et al.: Image transformer. In: ICML, pp. 4052–4061 (2018)

    Google Scholar 

  32. Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., Sorkine-Hornung, A.: Learning video object segmentation from static images. In: CVPR, pp. 2663–2672 (2017)

    Google Scholar 

  33. Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR, pp. 724–732 (2016)

    Google Scholar 

  34. Pont-Tuset, J., Perazzi, F., Caelles, S., Arbeláez, P., Sorkine-Hornung, A., Van Gool, L.: The 2017 Davis challenge on video object segmentation. arXiv preprint arXiv:1704.00675 (2017)

  35. Seong, H., Hyun, J., Kim, E.: Video multitask transformer network. In: ICCV Workshop (2019)

    Google Scholar 

  36. Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended CSSD. IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 717–729 (2015)

    Google Scholar 

  37. Shin Yoon, J., Rameau, F., Kim, J., Lee, S., Shin, S., So Kweon, I.: Pixel-level matching for video object segmentation using convolutional neural networks. In: CVPR, pp. 2167–2176 (2017)

    Google Scholar 

  38. Singh, K.K., Lee, Y.J.: Hide-and-seek: forcing a network to be meticulous for weakly-supervised object and action localization. In: ICCV, pp. 3544–3553. IEEE (2017)

    Google Scholar 

  39. Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: NIPS, pp. 2440–2448 (2015)

    Google Scholar 

  40. Tsai, Y.H., Yang, M.H., Black, M.J.: Video segmentation via object flow. In: CVPR, pp. 3899–3908 (2016)

    Google Scholar 

  41. Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998–6008 (2017)

    Google Scholar 

  42. Ventura, C., Bellver, M., Girbau, A., Salvador, A., Marques, F., Giro-i Nieto, X.: RVOS: end-to-end recurrent network for video object segmentation. In: CVPR, pp. 5277–5286 (2019)

    Google Scholar 

  43. Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., Chen, L.C.: FEELVOS: fast end-to-end embedding learning for video object segmentation. In: CVPR, pp. 9481–9490 (2019)

    Google Scholar 

  44. Voigtlaender, P., Leibe, B.: Online adaptation of convolutional neural networks for video object segmentation. In: BMVC (2017)

    Google Scholar 

  45. Wang, J., Jiang, H., Yuan, Z., Cheng, M.M., Hu, X., Zheng, N.: Salient object detection: a discriminative regional feature integration approach. Int. J. Comput. Vis. 123(2), 251–268 (2017)

    Google Scholar 

  46. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR, pp. 7794–7803 (2018)

    Google Scholar 

  47. Wang, Z., Xu, J., Liu, L., Zhu, F., Shao, L.: RANet: ranking attention network for fast video object segmentation. In: ICCV, October 2019

    Google Scholar 

  48. Wei, J., Wang, S., Wu, Z., Su, C., Huang, Q., Tian, Q.: Label decoupling framework for salient object detection. In: CVPR, pp. 13025–13034 (2020)

    Google Scholar 

  49. Xu, N., et al.: YouTube-VOS: sequence-to-sequence video object segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 603–619. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01228-1_36

    Chapter  Google Scholar 

  50. Yang, L., Wang, Y., Xiong, X., Yang, J., Katsaggelos, A.K.: Efficient video object segmentation via network modulation. In: CVPR, pp. 6499–6507 (2018)

    Google Scholar 

  51. Zeng, X., Liao, R., Gu, L., Xiong, Y., Fidler, S., Urtasun, R.: DMM-Net: differentiable mask-matching network for video object segmentation. In: ICCV, October 2019

    Google Scholar 

  52. Zhang, L., Lin, Z., Zhang, J., Lu, H., He, Y.: Fast video object segmentation via dynamic targeting network. In: ICCV, October 2019

    Google Scholar 

  53. Zhao, J.X., Liu, J.J., Fan, D.P., Cao, Y., Yang, J., Cheng, M.M.: EGNet: edge guidance network for salient object detection. In: ICCV, pp. 8779–8788 (2019)

    Google Scholar 

  54. Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.: Asymmetric non-local neural networks for semantic segmentation. In: ICCV, October 2019

    Google Scholar 

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Acknowledgement

This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7069370).

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea

    Hongje Seong, Junhyuk Hyun & Euntai Kim

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  1. Hongje Seong
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  2. Junhyuk Hyun
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  3. Euntai Kim
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Correspondence to Euntai Kim .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Seong, H., Hyun, J., Kim, E. (2020). Kernelized Memory Network for Video Object Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12367. Springer, Cham. https://doi.org/10.1007/978-3-030-58542-6_38

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  • DOI: https://doi.org/10.1007/978-3-030-58542-6_38

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