Abstract
Single human parsing aims at partitioning an image into semantically consistent regions belonging to the body parts or clothing items, which has gained remarkable improvement owing to a wide range of proposed methodologies. From the perspective of the loss design, besides the parsing loss of the final output, most existing studies target on exploiting multiple other losses to enhance parsing results, which is hard to make the model reach balanced condition by adjusting their ratios and may weaken the potential of some losses. In this work, we propose an edge enhancement module to emphasize the potential of edge loss and boundary information. At the same time, local and global information will be explored for complex multi-class human body parsing problem by densely connected atrous spatial pyramid pooling. This scheme results in a simple yet powerful Edge-Enhancement Network (EEN). Extensive experiments demonstrate that EEN achieves 56.55% mIoU on LIP dataset and 62.60% mIoU on CIHP dataset, which outperform the state-of-the-arts by 3.45% and 4.02%, respectively. The code of EEN is available at https://github.com/huangxi6/EEN.
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References
Bulò, S.R., Porzi, L., Kontschieder, P.: In-place activated BatchNorm for memory-optimized training of DNNs. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 5639–5647 (2018)
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
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)
Chen, L., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous convolution for semantic image segmentation. CoRR abs/1706.05587 (2017)
Chen, L., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 3640–3649 (2016)
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, Part VII. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.L.: Detect what you can: detecting and representing objects using holistic models and body parts. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 1979–1986 (2014)
Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2015)
Fang, H., Lu, G., Fang, X., Xie, J., Tai, Y., Lu, C.: Weakly and semi supervised human body part parsing via pose-guided knowledge transfer. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 70–78 (2018)
Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: universal human parsing via graph transfer learning. CoRR abs/1904.04536 (2019)
Gong, K., Liang, X., Li, Y., Chen, Y., Yang, M., Lin, L.: Instance-level human parsing via part grouping network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part IV. LNCS, vol. 11208, pp. 805–822. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_47
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: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 6757–6765 (2017)
Hou, Q., Liu, J., Cheng, M., Borji, A., Torr, P.H.S.: Three birds one stone: a unified framework for salient object segmentation, edge detection and skeleton extraction. CoRR abs/1803.09860 (2018)
Hu, Y., Chen, Y., Li, X., Feng, J.: Dynamic feature fusion for semantic edge detection. CoRR abs/1902.09104 (2019)
Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 5967–5976 (2017)
Liang, X., Gong, K., Shen, X., Lin, L.: Look into person: joint body parsing & pose estimation network and a new benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 871–885 (2019)
Liang, X., et al.: Human parsing with contextualized convolutional neural network. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, pp. 1386–1394 (2015)
Lin, G., Milan, A., Shen, C., Reid, I.D.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 5168–5177 (2017)
Liu, S., et al.: Matching-CNN meets KNN: quasi-parametric human parsing. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp. 1419–1427 (2015)
Liu, T., et al.: Devil in the details: towards accurate single and multiple human parsing. CoRR abs/1809.05996 (2018)
Lu, R., Zhou, M., Ming, A., Zhou, Y.: Context-constrained accurate contour extraction for occlusion edge detection. CoRR abs/1903.08890 (2019)
Luo, Y., Zheng, Z., Zheng, L., Guan, T., Yu, J., Yang, Y.: Macro-micro adversarial network for human parsing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part IX. LNCS, vol. 11213, pp. 424–440. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_26
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)
Nie, X., Feng, J., Zuo, Y., Yan, S.: Human pose estimation with parsing induced learner. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 2100–2108 (2018)
Simo-Serra, E., Fidler, S., Moreno-Noguer, F., Urtasun, R.: A high performance CRF model for clothes parsing. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014, Part III. LNCS, vol. 9005, pp. 64–81. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16811-1_5
Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: DenseASPP for semantic segmentation in street scenes. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 3684–3692 (2018)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 6230–6239 (2017)
Zhou, S., Wang, J., Wang, F., Huang, D.: SE2Net: Siamese edge-enhancement network for salient object detection. CoRR abs/1904.00048 (2019)
Zhu, S., Fidler, S., Urtasun, R., Lin, D., Loy, C.C.: Be your own Prada: fashion synthesis with structural coherence. In: IEEE International Conference on Computer Vision, ICCV 2017, pp. 1689–1697 (2017)
Acknowledgments
This work is supported by National Natural Science Foundation of China (grants No. 61672133 and No. 61832001).
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Huang, X., Wu, K., Hu, G., Shao, J. (2019). Multi-class Human Body Parsing with Edge-Enhancement Network. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_51
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