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The task of person re-identification (ReID) has attracted growing attention in recent years leading to improved performance, albeit with little focus on real-world applications.
Dynamic task decomposition for probabilistic tracking in complex scenes. In 2014 22nd International Conference on Pattern Recognition . IEEE, 4134–4139
Tao Hu, Stefano Messelodi, and Oswald Lanz. 2014 · 2014
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition . 815–823
Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015 · 2015
Earlier work this paper cites.
Scalable person re-identification: A benchmark. In Proceedings of the IEEE international conference on computer vision . 1116–1124
Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. 2015 · 2015
Earlier work this paper cites.
Performance measures and a data set for multi-target, multi-camera tracking. In European Conference on Computer Vision . Springer, 17–35
Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi. 2016 · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2818–2826
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Earlier work this paper cites.
In defense of the triplet loss for person re-identification
Alexander Hermans, Lucas Beyer, and Bastian Leibe. 2017 · 2017
Earlier work this paper cites.
Svdnet for pedestrian retrieval. In Proceedings of the IEEE International Conference on Computer Vision . 3800–3808
Yifan Sun, Liang Zheng, Weijian Deng, and Shengjin Wang. 2017 · 2017
Earlier work this paper cites.
Alignedreid: Surpassing human-level performance in person re-identification
Xuan Zhang, Hao Luo, Xing Fan, Weilai Xiang, Yixiao Sun, Qiqi Xiao, Wei Jiang, Chi Zhang, and Jian Sun. 2017 · 2017
Earlier work this paper cites.
Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. 2017 · 2017
Earlier work this paper cites.
Group consistent similarity learning via deep crf for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 8649–8658
Dapeng Chen, Dan Xu, Hongsheng Li, Nicu Sebe, and Xiaogang Wang. 2018 · 2018
Earlier work this paper cites.
Batch feature erasing for person re-identification and beyond
Zuozhuo Dai, Mingqiang Chen, Siyu Zhu, and Ping Tan. 2018 · 2018
Earlier work this paper cites.
Scpnet: Spatial-channel parallelism network for joint holistic and partial person re-identification. In Asian Conference on Computer Vision . Springer, 19–34
Xing Fan, Hao Luo, Xuan Zhang, Lingxiao He, Chi Zhang, and Wei Jiang. 2018 · 2018
Earlier work this paper cites.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. 2018 · 2018
Cited alongside, same era.
Human semantic parsing for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 1062–1071
Mahdi M Kalayeh, Emrah Basaran, Muhittin Gökmen, Mustafa E Kamasak, and Mubarak Shah. 2018 · 2018
Cited alongside, same era.
Harmonious attention network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2285–2294
Wei Li, Xiatian Zhu, and Shaogang Gong. 2018 · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European Conference on Computer Vision (ECCV) . 116–131
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun. 2018 · 2018
Cited alongside, same era.
A coarse-to-fine pyramidal model for person re-identification via multi-loss dynamic training
Feng Zheng, Xing Sun, Xinyang Jiang, Xiaowei Guo, Zongqiao Yu, and Feiyue Huang. 2018a · 2018
Later among the works it cites.
A discriminatively learned CNN embedding for person reidentification
Zhedong Zheng, Liang Zheng, and Yi Yang. 2018b · 2018
Later among the works it cites.
Camstyle: A novel data augmentation method for person re-identification
Zhun Zhong, Liang Zheng, Zhedong Zheng, Shaozi Li, and Yi Yang. 2018 · 2018
Later among the works it cites.
ABD-Net: Attentive but Diverse Person Re-Identification
Tianlong Chen, Shaojin Ding, Jingyi Xie, Ye Yuan, Wuyang Chen, Yang Yang, Zhou Ren, and Zhangyang Wang. 2019 · 2019
Closest in time.
Spherereid: Deep hypersphere manifold embedding for person re-identification
Xing Fan, Wei Jiang, Hao Luo, and Mengjuan Fei. 2019 · 2019
Closest in time.
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Lei Qi, Jing Huo, Lei Wang, Yinghuan Shi, and Yang Gao. 2018 · 2018
Cited alongside, same era.
Pose-normalized image generation for person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV) . 650–667
Xuelin Qian, Yanwei Fu, Tao Xiang, Wenxuan Wang, Jie Qiu, Yang Wu, Yu-Gang Jiang, and Xiangyang Xue. 2018 · 2018
Cited alongside, same era.
Fine-tuning CNN image retrieval with no human annotation
Filip Radenović, Giorgos Tolias, and Ondřej Chum. 2018 · 2018
Cited alongside, same era.
Features for multi-target multi-camera tracking and re-identification. In Proceedings of the IEEE conference on computer vision and pattern recognition . 6036–6046
Ergys Ristani and Carlo Tomasi. 2018 · 2018
Cited alongside, same era.
Dual attention matching network for context-aware feature sequence based person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 5363–5372
Jianlou Si, Honggang Zhang, Chun-Guang Li, Jason Kuen, Xiangfei Kong, Alex C Kot, and Gang Wang. 2018 · 2018
Cited alongside, same era.
Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In Proceedings of the European Conference on Computer Vision (ECCV) . 480–496
Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. 2018 · 2018
Cited alongside, same era.
Learning discriminative features with multiple granularities for person re-identification. In 2018 ACM Multimedia Conference on Multimedia Conference . ACM, 274–282
Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, and Xi Zhou. 2018a · 2018
Cited alongside, same era.
Local convolutional neural networks for person re-identification. In 2018 ACM Multimedia Conference on Multimedia Conference . ACM, 1074–1082
Jiwei Yang, Xu Shen, Xinmei Tian, Houqiang Li, Jianqiang Huang, and Xian-Sheng Hua. 2018 · 2018
Cited alongside, same era.
Bag of tricks and a strong baseline for deep person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . 0–0
Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, and Wei Jiang. 2019 · 2019
Closest in time.
A simple baseline for bayesian uncertainty in deep learning
Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, and Andrew Gordon Wilson. 2019 · 2019
Closest in time.
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
Ruijie Quan, Xuanyi Dong, Yu Wu, Linchao Zhu, and Yi Yang. 2019 · 2019
Closest in time.
Deep learning-based methods for person re-identification: A comprehensive review
Di Wu, Si-Jia Zheng, Xiao-Ping Zhang, Chang-An Yuan, Fei Cheng, Yang Zhao, Yong-Jun Lin, Zhong-Qiu Zhao, Yong-Li Jiang, and De-Shuang Huang. 2019 · 2019
Closest in time.
Relation-Aware Global Attention
Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Xin Jin, and Zhibo Chen. 2019b · 2019
Closest in time.
Joint discriminative and generative learning for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2138–2147
Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, and Jan Kautz. 2019 · 2019
Closest in time.
Omni-Scale Feature Learning for Person Re-Identification
Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, and Tao Xiang. 2019 · 2019
Closest in time.