{"type":"ScholarlyArticle","title":"Resolution-invariant Person Re-Identification","authors":[{"name":"Shunan Mao","slug":"shunan-mao","given":"Shunan","family":"Mao","affiliation":"Peking University"},{"name":"Shiliang Zhang","slug":"shiliang-zhang","given":"Shiliang","family":"Zhang","affiliation":"Peking University"},{"name":"Ming Yang","slug":"ming-yang","given":"Ming","family":"Yang","affiliation":"Horizon Robotics , Inc"}],"abstract":"Exploiting resolution invariant representation is critical for person Re-Identification (ReID) in real applications, where the resolutions of captured person images may vary dramatically. This paper learns person representations robust to resolution variance through jointly training a Foreground-Focus Super-Resolution (FFSR) module and a Resolution-Invariant Feature Extractor (RIFE) by end-to-end CNN learning. FFSR upscales the person foreground using a fully convolutional auto-encoder with skip connections learned with a foreground focus training loss. RIFE adopts two feature extraction streams weighted by a dual-attention block to learn features for low and high resolution images, respectively. These two complementary modules are jointly trained, leading to a strong resolution invariant representation. We evaluate our methods on five datasets containing person images at a large range of resolutions, where our methods show substantial superiority to existing solutions. For instance, we achieve Rank-1 accuracy of 36.4% and 73.3% on CAVIAR and MLR-CUHK03, outperforming the state-of-the art by 2.9% and 2.6%, respectively.","doi":"10.24963/ijcai.2019/124","url":"https://ijcai.org/proceedings/2019/124","pdf":"https://ijcai.org/proceedings/2019/0124.pdf","markdown":"https://ijcai.org/proceedings/2019/124.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/124","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Computer Vision","track":"Main track","number":124,"pages":{"first":883,"last":889},"keywords":["Computer Vision: Computer Vision","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2019] Yun-Chun Chen, Yu-Jhe Li, Xiao-fei Du, and Yu-Chiang Frank Wang. Learning resolution-invariant deep representations for person re-identification. In AAAI, 2019.","title":"Learning resolution-invariant deep representations for person re-identification","authors":"Yun-Chun Chen, Yu-Jhe Li, Xiao-Fei Du, Yu-Chiang Frank, Wang","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cheng et al., 2011] Dong Seon Cheng, Marco Cristani, Michele Stoppa, Loris Bazzani, and Vittorio Murino. Cus- tom pictorial structures for re-identification. In BMVC. Citeseer, 2011.","title":"Custom pictorial structures for re-identification","authors":"Marco Dong Seon Cheng, Michele Cristani, Loris Stoppa, Vittorio Bazzani, Murino","year":"2011","venue":"BMVC. Citeseer","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Dong et al., 2014] Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Learning a deep convolutional net- work for image super-resolution. In ECCV, pages 184- 199. Springer, 2014.","title":"Learning a deep convolutional network for image super-resolution","authors":"Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang","year":"2014","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. In NIPS, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gray and Tao, 2008] Douglas Gray and Hai Tao. Viewpoint invariant pedestrian recognition with an ensemble of local- ized features. In ECCV, pages 262-275. Springer, 2008.","title":"Viewpoint invariant pedestrian recognition with an ensemble of localized features","authors":"Tao ; Gray, Douglas Gray, Hai Tao","year":"2008","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu et al., 2018] Jie Hu, Li Shen, and Gang Sun. Squeeze- and-excitation networks. In CVPR, pages 7132-7141, 2018.","title":"Squeezeand-excitation networks","authors":"Jie Hu, Li Shen, Gang Sun","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huang et al., 2017] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. Densely con- nected convolutional networks. In CVPR, pages 2261- 2269, 2017.","title":"Densely connected convolutional networks","authors":"Gao Huang, Zhuang Liu, Laurens Van Der Maaten, Kilian Q Weinberger","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Insafutdinov et al., 2016] Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka, and Bernt Schiele. Deepercut: A deeper, stronger, and faster multi-person pose estimation model. In ECCV, pages 34-50. Springer, 2016.","title":"Deepercut: A deeper, stronger, and faster multi-person pose estimation model","authors":"Insafutdinov","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jaderberg et al., 2015] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. In NIPS, pages 2017-2025, 2015.","title":"Spatial transformer networks","authors":"Jaderberg","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jiao et al., 2018] Jiening Jiao, Wei-Shi Zheng, Ancong Wu, Xiatian Zhu, and Shaogang Gong. Deep low-resolution person re-identification. AAAI, 2018.","title":"Deep low-resolution person re-identification","authors":"Jiening Jiao, Wei-Shi Zheng, Ancong Wu, Xiatian Zhu, Shaogang Gong","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jing et al., 2015] Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Xinge You, Qinglong Liu, Dong Yue, Ruimin Hu, and Baowen Xu. Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning. In CVPR, pages 695-704, 2015.","title":"Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning","authors":"Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Xinge You, Qinglong Liu, Dong Yue, Ruimin Hu, Baowen Xu","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kim et al., 2016] Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, pages 1646-1654, 2016.","title":"Accurate image super-resolution using very deep convolutional networks","authors":"Jiwon Kim, Jung Kwon Lee, Kyoung Mu, Lee","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ledig et al., 2017] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic single image super- resolution using a generative adversarial network. In CVPR, volume 2, page 4, 2017.","title":"Photo-realistic single image superresolution using a generative adversarial network","authors":"Ledig","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2014] Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. Deepreid: Deep filter pairing neural network for person re-identification. In CVPR, pages 152-159, 2014.","title":"Deepreid: Deep filter pairing neural network for person re-identification","authors":"Wei Li, Rui Zhao, Tong Xiao, Xiaogang Wang","year":"2014","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2015] Xiang Li, Wei-Shi Zheng, Xiaojuan Wang, Tao Xiang, and Shaogang Gong. Multi-scale learning for low-resolution person re-identification. In ICCV, pages 3765-3773, 2015.","title":"Multi-scale learning for low-resolution person re-identification","authors":"Xiang Li, Wei-Shi Zheng, Xiaojuan Wang, Tao Xiang, Shaogang Gong","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2019] Jianing Li, Shiliang Zhang, and Tiejun Huang. Multi-scale 3d convolution network for video based person re-identification. In AAAI, 2019.","title":"Multi-scale 3d convolution network for video based person re-identification","authors":"Jianing Li, Shiliang Zhang, Tiejun Huang","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Mao et al., 2016] Xiaojiao Mao, Chunhua Shen, and Yu- Bin Yang. Image restoration using very deep convolution- al encoder-decoder networks with symmetric skip connec- tions. In NIPS, pages 2802-2810, 2016.","title":"Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections","authors":"Mao","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ronneberger et al., 2015] Olaf Ronneberger, Philipp Fisch- er, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, pages 234- 241. Springer, 2015.","title":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation","authors":"Ronneberger","year":"2015","venue":"MICCAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Russakovsky et al., 2015] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recogni- tion challenge. International Journal of Computer Vision, 115(3):211-252, 2015.","title":"Imagenet large scale visual recognition challenge","authors":"Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein","year":"2015","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tai et al., 2017] Ying Tai, Jian Yang, and Xiaoming Liu. Image super-resolution via deep recursive residual net- work. In CVPR, volume 1, page 5, 2017.","title":"Image super-resolution via deep recursive residual network","authors":"Ying Tai, Jian Yang, Xiaoming Liu","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2016] Zheng Wang, Ruimin Hu, Yi Yu, Junjun Jiang, Chao Liang, and Jinqiao Wang. Scale-adaptive low- resolution person re-identification via learning a discrimi- nating surface. In IJCAI, pages 2669-2675, 2016.","title":"Scale-adaptive lowresolution person re-identification via learning a discriminating surface","authors":"Zheng Wang, Ruimin Hu, Yi Yu, Junjun Jiang, Chao Liang, Jinqiao Wang","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2018a] Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Recovering realistic texture in im- age super-resolution by deep spatial feature transform. In CVPR, pages 606-615, 2018.","title":"Recovering realistic texture in image super-resolution by deep spatial feature transform","authors":"Wang","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2018b] Zheng Wang, Mang Ye, Fan Yang, Xi- ang Bai, and Shin'ichi Satoh. Cascaded sr-gan for scale- adaptive low resolution person re-identification. In IJCAI, pages 3891-3897, 2018.","title":"Cascaded sr-gan for scaleadaptive low resolution person re-identification","authors":"Wang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wei et al., 2018] Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. Person transfer gan to bridge domain gap for person re-identification. In CVPR, pages 79-88, 2018.","title":"Person transfer gan to bridge domain gap for person re-identification","authors":"Wei","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Yu et al., 2018] Xin Yu, Basura Fernando, Richard Hartley, and Fatih Porikli. Super-resolving very low-resolution face images with supplementary attributes. In CVPR, pages 908-917, 2018.","title":"Super-resolving very low-resolution face images with supplementary attributes","authors":"Xin Yu, Basura Fernando, Richard Hartley, Fatih Porikli","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zheng et al., 2015] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In ICCV, pages 1116-1124, 2015.","title":"Scalable person re-identification: A benchmark","authors":"Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, Qi Tian","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}