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Deep Novel View Synthesis from Colored 3D Point Clouds Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Blended Grammar Network for Human Parsing

Authors : Xiaomei Zhang, Yingying Chen, Bingke Zhu, Jinqiao Wang, Ming Tang

Published in: Computer Vision – ECCV 2020

Publisher: Springer International Publishing

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Abstract

Although human parsing has made great progress, it still faces a challenge, i.e., how to extract the whole foreground from similar or cluttered scenes effectively. In this paper, we propose a Blended Grammar Network (BGNet), to deal with the challenge. BGNet exploits the inherent hierarchical structure of a human body and the relationship of different human parts by means of grammar rules in both cascaded and paralleled manner. In this way, conspicuous parts, which are easily distinguished from the background, can amend the segmentation of inconspicuous ones, improving the foreground extraction. We also design a Part-aware Convolutional Recurrent Neural Network (PCRNN) to pass messages which are generated by grammar rules. To train PCRNNs effectively, we present a blended grammar loss to supervise the training of PCRNNs. We conduct extensive experiments to evaluate BGNet on PASCAL-Person-Part, LIP, and PPSS datasets. BGNet obtains state-of-the-art performance on these human parsing datasets.

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Literature
1.
2.
Chen, L.C., et al.: Searching for efficient multi-scale architectures for dense image prediction. In: NeurIPS, pp. 8699–8710 (2018)
3.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE TPAMI 40(4), 834–848 (2018)CrossRef
4.
Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: CVPR, June 2016
5.
Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: detecting and representing objects using holistic models and body parts. In: CVPR, pp. 1979–1986 (2014)
6.
Fang, H.S., Lu, G., Fang, X., Xie, J., Tai, Y.W., Lu, C.: Weakly and semi supervised human body part parsing via pose-guided knowledge transfer. arXiv preprint arXiv:​1805.​04310 (2018)
7.
Fang, H., Xu, Y., Wang, W., Liu, X., Zhu, S.C.: Learning pose grammar to encode human body configuration for 3D pose estimation. In: AAAI (2018)
8.
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: CVPR, pp. 2360–2367 (2010)
9.
Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3146–3154 (2019)
10.
Fu, J., Liu, J., Wang, Y., Lu, H.: Densely connected deconvolutional network for semantic segmentation. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3085–3089. IEEE (2017)
11.
Garland-Thomson, R.: Staring: How We Look. Oxford University Press, Oxford (2009)
12.
Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: universal human parsing via graph transfer learning. In: CVPR (2019)
13.
Gong, K., Liang, X., Li, Y., Chen, Y., Yang, M., Lin, L.: Instance-level human parsing via part grouping network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 770–785 (2018)
14.
Gong, K., Liang, X., Zhang, D., Shen, X., Lin, L.: Look into person: self-supervised structure-sensitive learning and a new benchmark for human parsing. In: CVPR, vol. 2, p. 6 (2017)
15.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
16.
Ke, L., Chang, M.C., Qi, H., Lyu, S.: Multi-scale structure-aware network for human pose estimation. In: ECCV, pp. 713–728 (2018)
17.
18.
Liang, X., Gong, K., Shen, X., Lin, L.: Look into person: joint body parsing & pose estimation network and a new benchmark. IEEE TPAMI 41, 871–885 (2018)CrossRef
19.
Liang, X., Lin, L., Shen, X., Feng, J., Yan, S., Xing, E.P.: Interpretable structure-evolving LSTM. In: CVPR, pp. 2175–2184 (2017)
20.
Liang, X., Shen, X., Xiang, D., Feng, J., Lin, L., Yan, S.: Semantic object parsing with local-global long short-term memory. In: CVPR, pp. 3185–3193 (2016)
21.
Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: CVPR, pp. 1925–1934 (2017)
22.
Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.T.: Cross-domain complementary learning with synthetic data for multi-person part segmentation. arXiv preprint arXiv:​1907.​05193 (2019)
23.
Liu, T., et al.: Devil in the details: towards accurate single and multiple human parsing. In: AAAI (2019)
24.
Liu, X., Zhang, M., Liu, W., Song, J., Mei, T.: BraidNet: braiding semantics and details for accurate human parsing. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 338–346. ACM (2019)
25.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
26.
Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. arXiv preprint arXiv:​1611.​08408 (2016)
27.
Luo, P., Wang, X., Tang, X.: Pedestrian parsing via deep decompositional network. In: CVPR, pp. 2648–2655 (2014)
28.
Luo, Y., Zheng, Z., Zheng, L., Guan, T., Yu, J., Yang, Y.: Macro-micro adversarial network for human parsing. In: ECCV (2018)
29.
Nie, X., Feng, J., Yan, S.: Mutual learning to adapt for joint human parsing and pose estimation. In: ECCV, pp. 502–517 (2018)
30.
Park, S., Nie, B.X., Zhu, S.C.: Attribute and-or grammar for joint parsing of human pose, parts and attributes. IEEE TPAMI 40(7), 1555–1569 (2018)CrossRef
31.
Qi, S., Huang, S., Wei, P., Zhu, S.C.: Predicting human activities using stochastic grammar. In: CVPR (2017)
32.
33.
Thorpe, S., Fize, D., Marlot, C.: Speed of processing in the human visual system. Nature 381(6582), 520 (1996)CrossRef
34.
Wang, P., Shen, X., Lin, Z., Cohen, S., Price, B., Yuille, A.: Joint object and part segmentation using deep learned potentials. In: ICCV, pp. 1573–1581 (2015)
35.
Wang, W., Xu, Y., Shen, J., Zhu, S.: Attentive fashion grammar network for fashion landmark detection and clothing category classification. In: CVPR (2018)
36.
Wang, W., Zhang, Z., Qi, S., Shen, J., Pang, Y., Shao, L.: Learning compositional neural information fusion for human parsing. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)
37.
Wang, Y., Duan, T., Liao, Z., Forsyth, D.: Discriminative hierarchical part-based models for human parsing and action recognition. J. Mach. Learn. Res. 13(1), 3075–3102 (2012)MathSciNetMATH
38.
Xia, F., Wang, P., Chen, L.C., Yuille, A.L.: Zoom better to see clearer: human and object parsing with hierarchical auto-zoom net. In: ICCV, pp. 648–663 (2015)
39.
Xia, F., Wang, P., Chen, X., Yuille, A.: Joint multi-person pose estimation and semantic part segmentation. In: CVPRW, pp. 6080–6089 (2017)
40.
Yamaguchi, K., Kiapour, M.H., Berg, T.L.: Paper doll parsing: retrieving similar styles to parse clothing items. In: ICCV, pp. 3519–3526 (2013)
41.
Zhang, X., Chen, Y., Zhu, B., Wang, J., Tang, M.: Part-aware context network for human parsing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8971–8980 (2020)
42.
Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: ICNet for real-time semantic segmentation on high-resolution images. In: ECCV, pp. 405–420 (2018)
43.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)
44.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR, pp. 2921–2929 (2016)
45.
Zhu, B., Chen, Y., Tang, M., Wang, J.: Progressive cognitive human parsing. In: AAAI (2018)
Metadata
Title
Blended Grammar Network for Human Parsing
Authors
Xiaomei Zhang
Yingying Chen
Bingke Zhu
Jinqiao Wang
Ming Tang
Copyright Year
2020
Book
Computer Vision – ECCV 2020

Print ISBN: 978-3-030-58585-3

Electronic ISBN: 978-3-030-58586-0

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-58586-0_12

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