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Fast Single Shot Instance Segmentation

  • Zuoxin Li
  • Fuqiang ZhouEmail author
  • Lu Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11364)

Abstract

In this work, we propose fast single shot instance segmentation framework (FSSI), which aims at jointly object detection, segmenting and distinguishing every individual instance (instance segmentation) in a flexible and fast way. In the pipeline of FSSI, the instance segmentation task is divided into three parallel sub-tasks: object detection, semantic segmentation, and direction prediction. The instance segmentation result is then generated from these three sub-tasks’ results by the post-process in parallel. In order to accelerate the process, the SSD-like detection structure and two-path architecture which can generate more accurate segmentation prediction without heavy calculation burden are adopted. Our experiments on the PASCAL VOC and the MSCOCO datasets demonstrate the benefits of our approach, which accelerate the instance segmentation process with competitive result compared to MaskRCNN. Code is public available (https://github.com/lzx1413/FSSI).

Keywords

Instance segmentation Multi-task learning Convolutional Neural Networks 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61471123).

References

  1. 1.
    Arbeláez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 328–335 (2014)Google Scholar
  2. 2.
    Arnab, A., Jayasumana, S., Zheng, S., Torr, P.H.S.: Higher order conditional random fields in deep neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 524–540. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46475-6_33CrossRefGoogle Scholar
  3. 3.
    Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)CrossRefGoogle Scholar
  4. 4.
    Dai, J., He, K., Li, Y., Ren, S., Sun, J.: Instance-sensitive fully convolutional networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 534–549. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46466-4_32CrossRefGoogle Scholar
  5. 5.
    Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: CVPR (2016)Google Scholar
  6. 6.
    Dvornik, N., Shmelkov, K., Mairal, J., Schmid, C.: BlitzNet: a real-time deep network for scene understanding. In: ICCV (2017)Google Scholar
  7. 7.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
  8. 8.
    Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: 2011 International Conference on Computer Vision, pp. 991–998 (2011)Google Scholar
  9. 9.
    Hariharan, B., Arbelaez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2015)Google Scholar
  10. 10.
    Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 297–312. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10584-0_20CrossRefGoogle Scholar
  11. 11.
    Hayder, Z., He, X., Salzmann, M.: Boundary-aware instance segmentation. In: CVPR (2017)Google Scholar
  12. 12.
    He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)Google Scholar
  13. 13.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  14. 14.
    Jeong, J., Park, H., Kwak, N.: Enhancement of SSD by concatenating feature maps for object detection. CoRR abs/1705.09587 (2017)Google Scholar
  15. 15.
    Kong, T., Yao, A., Chen, Y., Sun, F.: HyperNet: towards accurate region proposal generation and joint object detection. In: CVPR, pp. 845–853 (2016). 00026Google Scholar
  16. 16.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)CrossRefGoogle Scholar
  17. 17.
    Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: Fully convolutional instance-aware semantic segmentation. In: CVPR (2017)Google Scholar
  18. 18.
    Li, Z., Zhou, F.: FSSD: feature fusion single shot multibox detector. CoRR abs/1712.00960 (2017)Google Scholar
  19. 19.
    Liang, X., Wei, Y., Shen, X., Yang, J., Lin, L., Yan, S.: Proposal-free network for instance-level object segmentation. CoRR abs/1509.02636 (2015)Google Scholar
  20. 20.
    Lin, T., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. CoRR abs/1612.03144 (2016)Google Scholar
  21. 21.
    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. 01470CrossRefGoogle Scholar
  22. 22.
    Liu, S., Qi, X., Shi, J., Zhang, H., Jia, J.: Multi-scale patch aggregation (MPA) for simultaneous detection and segmentation. In: CVPR (2016)Google Scholar
  23. 23.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  24. 24.
    Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: Semantic image segmentation via deep parsing network. In: ICCV (2015)Google Scholar
  25. 25.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)Google Scholar
  26. 26.
    Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with restarts. CoRR abs/1608.03983 (2016)Google Scholar
  27. 27.
    Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: ICCV (2015)Google Scholar
  28. 28.
    Pinheiro, P.O., Lin, T.-Y., Collobert, R., Dollár, P.: Learning to refine object segments. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 75–91. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_5CrossRefGoogle Scholar
  29. 29.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 91–99. Curran Associates, Inc. (2015)Google Scholar
  30. 30.
    Shen, Z., Liu, Z., Li, J., Jiang, Y.G., Chen, Y., Xue, X.: DSOD: learning deeply supervised object detectors from scratch. In: ICCV (2017)Google Scholar
  31. 31.
    Uhrig, J., Cordts, M., Franke, U., Brox, T.: Pixel-level encoding and depth layering for instance-level semantic labeling. In: Rosenhahn, B., Andres, B. (eds.) GCPR 2016. LNCS, vol. 9796, pp. 14–25. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45886-1_2CrossRefGoogle Scholar
  32. 32.
    Vemulapalli, R., Tuzel, O., Liu, M.Y., Chellapa, R.: Gaussian conditional random field network for semantic segmentation. In: CVPR (2016)Google Scholar
  33. 33.
    Wu, Z., Shen, C., van den Hengel, A.: Bridging category-level and instance-level semantic image segmentation. CoRR abs/1605.06885 (2016)Google Scholar
  34. 34.
    Zhang, Z., Fidler, S., Urtasun, R.: Instance-level segmentation for autonomous driving with deep densely connected MRFs. In: CVPR (2016)Google Scholar
  35. 35.
    Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Beihang UniversityBeijingChina
  2. 2.Beijing University of Posts and TelecommunicationBeijingChina

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