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Double Granularity Relation Network with Self-criticism for Occluded Person Re-identification

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Book cover MultiMedia Modeling (MMM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13141))

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Abstract

Occluded person Re-identification is still a challenge. Most existing methods capture visible human parts based on external cues, such as human pose and semantic mask. In this paper, we propose a double granularity relation network with self-criticism to locate visible human parts. We learn the region-wise relation between part and whole and pixel-wise relation between pixel and whole. The relations find non-occluded human body parts and exclude noisy information. To guide the relation learning, we introduce two relation critic losses, which score the parts and maximize the performance by imposing higher weights on large parts and lower ones on small parts. We design the double branch model based on the proposed critic loss and evaluate it on the popular benchmarks. The experimental results show the superiority of our method, which achieves mAP of 51.0% and 75.4% respectively on Occluded-DukeMTMC and P-DukeMTMC-reID. Our codes are available at DRNC.

This work is supported in part by the National Key Research and Development Plan of China(No.2018YFB0804202) and in part by the National Natural Science Foundation of China (No. 61672495, No. U1736218 and No.2019YFB1005201).

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References

  1. Chen, T., et al.: ABD-Net: attentive but diverse person re-identification. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8350–8360 (2019)

    Google Scholar 

  2. Chen, Y., Wang, H., Sun, X., Fan, B., Tang, C.: Deep attention aware feature learning for person re-identification. arXiv abs/2003.00517 (2020)

    Google Scholar 

  3. Gao, S., Wang, J., Lu, H., Liu, Z.: Pose-guided visible part matching for occluded person ReID. arXiv abs/2004.00230 (2020)

    Google Scholar 

  4. Ge, Y., et al.: FD-GAN: pose-guided feature distilling GAN for robust person re-identification. In: NeurIPS (2018)

    Google Scholar 

  5. Guo, J., Yuan, Y., Huang, L., Zhang, C., Yao, J.G., Han, K.: Beyond human parts: dual part-aligned representations for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3642–3651 (2019)

    Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, June 2016. https://doi.org/10.1109/CVPR.2016.90

  7. He, L., Liang, J., Li, H., Sun, Z.: Deep spatial feature reconstruction for partial person re-identification: alignment-free approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7073–7082 (2018)

    Google Scholar 

  8. He, L., Sun, Z., Zhu, Y., Wang, Y.: Recognizing partial biometric patterns. arXiv preprint arXiv:1810.07399 (2018)

  9. He, L., et al.: Foreground-aware pyramid reconstruction for alignment-free occluded person re-identification. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8449–8458 (2019)

    Google Scholar 

  10. Huang, H., Li, D., Zhang, Z., Chen, X., Huang, K.: Adversarially occluded samples for person re-identification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5098–5107 (2018)

    Google Scholar 

  11. Jia, M., et al.: Matching on sets: conquer occluded person re-identification without alignment. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 1673–1681 (2021)

    Google Scholar 

  12. Kalayeh, M.M., Basaran, E., Gökmen, M., Kamasak, M.E., Shah, M.: Human semantic parsing for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1062–1071 (2018)

    Google Scholar 

  13. Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2

    Chapter  Google Scholar 

  14. Liu, J., Ni, B., Yan, Y., Zhou, P., Cheng, S., Hu, J.: Pose transferrable person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4099–4108 (2018)

    Google Scholar 

  15. Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0 (2019)

    Google Scholar 

  16. Miao, J., Wu, Y., Liu, P., Ding, Y., Yang, Y.: Pose-guided feature alignment for occluded person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 542–551 (2019)

    Google Scholar 

  17. Park, H., Ham, B.: Relation network for person re-identification. In: AAAI (2020)

    Google Scholar 

  18. Qian, X., et al.: Pose-normalized image generation for person re-identification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 661–678. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_40

    Chapter  Google Scholar 

  19. Quispe, R., Pedrini, H.: Improved person re-identification based on saliency and semantic parsing with deep neural network models. Image Vis. Comput. 92, 103809 (2019)

    Article  Google Scholar 

  20. Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128, 336–359 (2019)

    Article  Google Scholar 

  21. Song, C., Huang, Y., Ouyang, W., Wang, L.: Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1179–1188 (2018)

    Google Scholar 

  22. Sun, Y., et al.: Perceive where to focus: learning visibility-aware part-level features for partial person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 393–402 (2019)

    Google Scholar 

  23. Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501–518. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_30

    Chapter  Google Scholar 

  24. Tay, C., Roy, S., Yap, K.: AANet: attribute attention network for person re-identifications. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7127–7136 (2019)

    Google Scholar 

  25. Wang, G., et al.: High-order information matters: learning relation and topology for occluded person re-identification. arXiv abs/2003.08177 (2020)

    Google Scholar 

  26. Xu, J., Zhao, R., Zhu, F., Wang, H., Ouyang, W.: Attention-aware compositional network for person re-identification. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2119–2128 (2018)

    Google Scholar 

  27. Ye, M., et al.: Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing. IEEE Trans. Multimedia 18(12), 2553–2566 (2016)

    Article  Google Scholar 

  28. Zhang, X., et al.: AlignedReID: surpassing human-level performance in person re-identification. arXiv preprint arXiv:1711.08184 (2017)

  29. Zhang, Z., Lan, C., Zeng, W., Jin, X., Chen, Z.: Relation-aware global attention for person re-identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3183–3192 (2020)

    Google Scholar 

  30. Zheng, W.S., Li, X., Xiang, T., Liao, S., Lai, J., Gong, S.: Partial person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4678–4686 (2015)

    Google Scholar 

  31. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person reidentification: A benchmark. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1116–1124 (2015)

    Google Scholar 

  32. Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1318–1327 (2017)

    Google Scholar 

  33. Zhuo, J., Chen, Z., Lai, J., Wang, G.: Occluded person re-identification (2018)

    Google Scholar 

  34. Zhuo, J., Lai, J., Chen, P.: A novel teacher-student learning framework for occluded person re-identification. arXiv abs/1907.03253 (2019)

    Google Scholar 

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Ren, X., Zhang, D., Bao, X., Shi, L. (2022). Double Granularity Relation Network with Self-criticism for Occluded Person Re-identification. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13141. Springer, Cham. https://doi.org/10.1007/978-3-030-98358-1_26

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  • DOI: https://doi.org/10.1007/978-3-030-98358-1_26

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