Abstract
The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. Source code available at https://github.com/yanfengliu/layered_embeddings.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Chen, Y.T., Liu, X., Yang, M.H.: Multi-instance object segmentation with occlusion handling. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3470–3478, June 2015
De Brabandere, B., Neven, D., Van Gool, L.: Semantic instance segmentation with a discriminative loss function. In: Deep Learning for Robotic Vision, Workshop at CVPR 2017, pp. 1–2. CVPR (2017)
Ehsani, K., Mottaghi, R., Farhadi, A.: SeGAN: segmenting and generating the invisible. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6144–6153 (2018)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)
Fathi, A., et al.: Semantic instance segmentation via deep metric learning. arXiv preprint arXiv:1703.10277 (2017)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. IEEE (2017)
Li, K., Malik, J.: Amodal instance segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 677–693. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_42
Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: Fully convolutional instance-aware semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2359–2367 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
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_2
Yang, Y., Hallman, S., Ramanan, D., Fowlkes, C.: Layered object detection for multi-class segmentation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3113–3120, June 2010
Yang, Y., Hallman, S., Ramanan, D., Fowlkes, C.: Layered object models for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1731–1743 (2012)
Zhu, Y., Tian, Y., Metaxas, D., Dollár, P.: Semantic amodal segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1464–1472 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Y., Psota, E.T., Pérez, L.C. (2019). Layered Embeddings for Amodal Instance Segmentation. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11662. Springer, Cham. https://doi.org/10.1007/978-3-030-27202-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-27202-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27201-2
Online ISBN: 978-3-030-27202-9
eBook Packages: Computer ScienceComputer Science (R0)