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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Amodal Instance Segmentation

verfasst von : Ke Li, Jitendra Malik

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

We consider the problem of amodal instance segmentation, the objective of which is to predict the region encompassing both visible and occluded parts of each object. Thus far, the lack of publicly available amodal segmentation annotations has stymied the development of amodal segmentation methods. In this paper, we sidestep this issue by relying solely on standard modal instance segmentation annotations to train our model. The result is a new method for amodal instance segmentation, which represents the first such method to the best of our knowledge. We demonstrate the proposed method’s effectiveness both qualitatively and quantitatively.

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Literatur
1.
Arbeláez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L., Malik, J.: Semantic segmentation using regions and parts. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3378–3385. IEEE (2012)
2.
Bell, S., Upchurch, P., Snavely, N., Bala, K.: OpenSurfaces: a richly annotated catalog of surface appearance. ACM Trans. Graph. (TOG) 32(4), 111 (2013)CrossRef
3.
Boix, X., Gonfaus, J.M., Van de Weijer, J., Bagdanov, A.D., Serrat, J., Gonzàlez, J.: Harmony potentials. Int. J. Comput. Vis. 96(1), 83–102 (2012)MathSciNetCrossRefMATH
4.
Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 109–122. Springer, Heidelberg (2002)CrossRef
5.
Carreira, J., Li, F., Sminchisescu, C.: Object recognition by sequential figure-ground ranking. Int. J. Comput. Vis. 98(3), 243–262 (2012)MathSciNetCrossRef
6.
Dai, J., He, K., Sun, J.: Convolutional feature masking for joint object and stuff segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3992–4000 (2015)
7.
Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades (2015). arXiv preprint: arXiv:​1512.​04412
8.
Dai, Q., Hoiem, D.: Learning to localize detected objects. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3322–3329. IEEE (2012)
9.
Dong, J., Chen, Q., Yan, S., Yuille, A.: Towards unified object detection and semantic segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 299–314. Springer, Heidelberg (2014)
10.
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)CrossRef
11.
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRef
12.
Fidler, S., Mottaghi, R., Yuille, A., Urtasun, R.: Bottom-up segmentation for top-down detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3294–3301 (2013)
13.
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
14.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
15.
Gu, C., Lim, J.J., Arbeláez, P., Malik, J.: Recognition using regions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1030–1037. IEEE (2009)
16.
Gupta, S., Arbelaez, P., Malik, J.: Perceptual organization and recognition of indoor scenes from RGB-D images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 564–571 (2013)
17.
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 991–998. IEEE (2011)
18.
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 297–312. Springer, Heidelberg (2014)
19.
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2015)
20.
Kanizsa, G.: Organization in Vision: Essays on Gestalt Perception. Praeger Publishers, New York (1979)
21.
Kar, A., Tulsiani, S., Carreira, J., Malik, J.: Amodal completion and size constancy in natural scenes. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 127–135 (2015)
22.
Kohli, P., Kumar, M.P.: Energy minimization for linear envelope MRFs. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1863–1870. IEEE (2010)
23.
Kumar, M.P., Ton, P., Zisserman, A.: Obj cut. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 18–25. IEEE (2005)
24.
Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: Graph cut based inference with co-occurrence statistics. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 239–253. Springer, Heidelberg (2010)CrossRef
25.
Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 2, p. 7 (2004)
26.
Levin, A., Weiss, Y.: Learning to combine bottom-up and top-down segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 581–594. Springer, Heidelberg (2006)CrossRef
27.
Li, K., Hariharan, B., Malik, J.: Iterative instance segmentation. In: CVPR (2016)
28.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Heidelberg (2014)
29.
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)
30.
Parkhi, O.M., Vedaldi, A., Jawahar, C., Zisserman, A.: The truth about cats and dogs. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1427–1434. IEEE (2011)
31.
Pinheiro, P., Collobert, R.: Recurrent convolutional neural networks for scene labeling. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 82–90 (2014)
32.
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)
33.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint: arXiv:​1409.​1556
34.
Tighe, J., Niethammer, M., Lazebnik, S.: Scene parsing with object instances and occlusion ordering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3748–3755 (2014)
35.
Vijayanarasimhan, S., Grauman, K.: Efficient region search for object detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1401–1408. IEEE (2011)
36.
Xiang, Y., Mottaghi, R., Savarese, S.: Beyond PASCAL: a benchmark for 3D object detection in the wild. In: IEEE Winter Conference on Applications of Computer Vision, pp. 75–82. IEEE (2014)
37.
Yang, Y., Hallman, S., Ramanan, D., Fowlkes, C.C.: Layered object models for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1731–1743 (2012)CrossRef
38.
Yu, S.X., Shi, J.: Object-specific figure-ground segregation. In: Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II–39. IEEE (2003)
39.
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1537 (2015)
40.
Metadaten
Titel
Amodal Instance Segmentation
verfasst von
Ke Li
Jitendra Malik
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46474-9

Electronic ISBN: 978-3-319-46475-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46475-6_42