Learning Discriminative Features with Multiple Granularities for Person Re-Identification

ABSTRACT

The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we obtain a top result of Rank-1/mAP=96.6%/94.2% with this method after re-ranking.

References

1. Ejaz Ahmed, Michael Jones, and Tim K Marks. 2015. An improved deep learning architecture for person re-identification. In CVPR. 3908--3916.
2. Jon Almazan, Bojana Gajic, Naila Murray, and Diane Larlus. 2018. Re-ID done right: towards good practices for person re-identification. arXiv preprint arXiv:1801.05339 (2018).
3. Xiang Bai, Mingkun Yang, Tengteng Huang, Zhiyong Dou, Rui Yu, and Yongchao Xu. 2017. Deep-Person: Learning Discriminative Deep Features for Person Re-Identification. arXiv preprint arXiv:1711.10658 (2017).
4. Xiaobin Chang, Timothy M. Hospedales, and Tao Xiang. 2018. Multi-Level Factorisation Net for Person Re-Identification. In CVPR. 2109--2118.
5. Dapeng Chen, Dan Xu, Hongsheng Li, Nicu Sebe, and Xiaogang Wang. 2018. Group Consistent Similarity Learning via Deep CRF for Person Re-Identification. In CVPR. 8649--8658.
6. Weihua Chen, Xiaotang Chen, Jianguo Zhang, and Kaiqi Huang. 2017a. Beyond triplet loss: a deep quadruplet network for person re-identification. In CVPR. 403--412.
7. Yanbei Chen, Xiatian Zhu, and Shaogang Gong. 2017b. Person Re-Identification by Deep Learning Multi-Scale Representations. In ICCV. 2590--2600.
8. De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, and Nanning Zheng. 2016. Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In CVPR. 1335--1344.
9. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In CVPR. 248--255.
10. Pedro Felzenszwalb, David McAllester, and Deva Ramanan. 2008. A discriminatively trained, multiscale, deformable part model. In CVPR. 1--8.
11. Ross Girshick. 2015. Fast r-cnn. In ICCV. 1440--1448.
12. Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006. Dimensionality reduction by learning an invariant mapping. In CVPR, Vol. 2. 1735--1742.
13. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.
14. Alexander Hermans, Lucas Beyer, and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017).
15. Elad Hoffer and Nir Ailon. 2015. Deep metric learning using triplet network. In International Workshop on Similarity-Based Pattern Recognition. Springer, 84--92.
16. Houjing Huang, Dangwei Li, Zhang Zhang, Xiaotang Chen, and Kaiqi Huang. 2018. Adversarially Occluded Samples for Person Re-Identification. In CVPR. 5098--5107.
17. Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML. 448--456.
18. Max Jaderberg, Karen Simonyan, Andrew Zisserman, et almbox. 2015. Spatial transformer networks. In NIPS. 2017--2025.
19. Dangwei Li, Xiaotang Chen, Zhang Zhang, and Kaiqi Huang. 2017a. Learning deep context-aware features over body and latent parts for person re-identification. In CVPR. 384--393.
20. Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. 2014. Deepreid: Deep filter pairing neural network for person re-identification. In CVPR. 152--159.
21. Wei Li, Xiatian Zhu, and Shaogang Gong. 2017b. Person re-identification by deep joint learning of multi-loss classification. In IJCAI. 2194--2200.
22. Wei Li, Xiatian Zhu, and Shaogang Gong. 2018. Harmonious Attention Network for Person Re-Identification. In CVPR. 2285--2294.
23. Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z Li. 2015. Person re-identification by local maximal occurrence representation and metric learning. In CVPR. 2197--2206.
24. Hao Liu, Jiashi Feng, Meibin Qi, Jianguo Jiang, and Shuicheng Yan. 2017a. End-to-end comparative attention networks for person re-identification. IEEE Transactions on Image Processing, Vol. 26, 7 (2017), 3492--3506.
25. Xihui Liu, Haiyu Zhao, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, and Xiaogang Wang. 2017b. HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis. In CVPR. 350--359.
26. Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi. 2016. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking. 17--35.
27. M. Saquib Sarfraz, Arne Schumann, Andreas Eberle, and Rainer Stiefelhagen. 2018. A Pose-Sensitive Embedding for Person Re-Identification With Expanded Cross Neighborhood Re-Ranking. In CVPR. 420--429.
28. Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In CVPR. 815--823.
29. Yantao Shen, Hongsheng Li, Tong Xiao, Shuai Yi, Dapeng Chen, and Xiaogang Wang. 2018a. Deep Group-Shuffling Random Walk for Person Re-Identification. In CVPR. 2265--2274.
30. Yantao Shen, Tong Xiao, Hongsheng Li, Shuai Yi, and Xiaogang Wang. 2018b. End-to-End Deep Kronecker-Product Matching for Person Re-Identification. In CVPR. 6886--6895.
31. Jianlou Si, Honggang Zhang, Chun-Guang Li, Jason Kuen, Xiangfei Kong, Alex C. Kot, and Gang Wang. 2018. Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-Identification. In CVPR. 5363--5372.
32. Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese. 2016. Deep metric learning via lifted structured feature embedding. In CVPR. 4004--4012.
33. Chi Su, Jianing Li, Shiliang Zhang, Junliang Xing, Wen Gao, and Qi Tian. 2017. Pose-driven Deep Convolutional Model for Person Re-identification. In ICCV. 3980--3989.
34. Yi Sun, Xiaogang Wang, and Xiaoou Tang. 2015. Deeply learned face representations are sparse, selective, and robust. In CVPR. 2892--2900.
35. Yifan Sun, Liang Zheng, Weijian Deng, and Shengjin Wang. 2017. SVDNet for Pedestrian Retrieval. In ICCV. 2590--2600.
36. Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. 2018. Beyond Part Models: Person Retrieval with Refined Part Pooling. In ECCV. In press.
37. Rahul Rama Varior, Mrinal Haloi, and Gang Wang. 2016. Gated siamese convolutional neural network architecture for human re-identification. In ECCV. Springer, 791--808.
38. Feng Wang, Xiang Xiang, Jian Cheng, and Alan Loddon Yuille. 2017. Normface: l2 hypersphere embedding for face verification. In 2017 ACM on Multimedia Conference. 1041--1049.
39. Tong Xiao, Hongsheng Li, Wanli Ouyang, and Xiaogang Wang. 2016. Learning deep feature representations with domain guided dropout for person re-identification. In CVPR. 1249--1258.
40. Jing Xu, Rui Zhao, Feng Zhu, Huaming Wang, and Wanli Ouyang. 2018. Attention-Aware Compositional Network for Person Re-Identification. In CVPR. 2119--2128.
41. Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, and Qi Tian. 2017. Deep representation learning with part loss for person re-identification. arXiv preprint arXiv:1707.00798 (2017).
42. Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. 2014. Deep metric learning for person re-identification. In ICPR. 34--39.
43. Xuan Zhang, Hao Luo, Xing Fan, Weilai Xiang, Yixiao Sun, Qiqi Xiao, Wei Jiang, Chi Zhang, and Jian Sun. 2017. Alignedreid: Surpassing human-level performance in person re-identification. arXiv preprint arXiv:1711.08184 (2017).
44. Haiyu Zhao, Maoqing Tian, Shuyang Sun, Jing Shao, Junjie Yan, Shuai Yi, Xiaogang Wang, and Xiaoou Tang. 2017b. Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In CVPR. 1077--1085.
45. Liming Zhao, Xi Li, Jingdong Wang, and Yueting Zhuang. 2017a. Deeply-learned part-aligned representations for person re-identification. In ICCV. 3219--3228.
46. Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. 2015. Scalable Person Re-identification: A Benchmark. In ICCV. 1116--1124.
47. Liang Zheng, Yi Yang, and Alexander G Hauptmann. 2016. Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984 (2016).
48. Zhedong Zheng, Liang Zheng, and Yi Yang. 2017a. Pedestrian alignment network for large-scale person re-identification. arXiv preprint arXiv:1707.00408 (2017).
49. Zhedong Zheng, Liang Zheng, and Yi Yang. 2017b. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. In ICCV. 3774--3782.
50. Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li. 2017. Re-ranking person re-identification with k-reciprocal encoding. In CVPR. 3652--3661.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Object identification

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Learning to rank

        • Supervised learning by classification

    • Machine learning approaches

      • Neural networks