{"type":"ScholarlyArticle","title":"Progressive Transfer Learning for Person Re-identification","authors":[{"name":"Zhengxu Yu","slug":"zhengxu-yu","given":"Zhengxu","family":"Yu","affiliation":"State Key Lab of CAD&CG , Zhejiang University , Hangzhou , China,"},{"name":"Zhongming Jin","slug":"zhongming-jin","given":"Zhongming","family":"Jin","affiliation":"DAMO Academy , Alibaba Group , Hangzhou , China,"},{"name":"Long Wei","slug":"long-wei","given":"Long","family":"Wei","affiliation":"State Key Lab of CAD&CG , Zhejiang University , Hangzhou , China,"},{"name":"Jishun Guo","slug":"jishun-guo","given":"Jishun","family":"Guo","affiliation":"GAC R&D Center , Guangzhou , China"},{"name":"Jianqiang Huang","slug":"jianqiang-huang","given":"Jianqiang","family":"Huang","affiliation":"DAMO Academy , Alibaba Group , Hangzhou , China,"},{"name":"Deng Cai","slug":"deng-cai","given":"Deng","family":"Cai","affiliation":"State Key Lab of CAD&CG , Zhejiang University , Hangzhou , China,"},{"name":"Xiaofei He","slug":"xiaofei-he","given":"Xiaofei","family":"He","affiliation":"State Key Lab of CAD&CG , Zhejiang University , Hangzhou , China,; Fabu Inc. , Hangzhou , China,"},{"name":"Xian-Sheng Hua","slug":"xian-sheng-hua","given":"Xian-Sheng","family":"Hua","affiliation":"DAMO Academy , Alibaba Group , Hangzhou , China,"}],"abstract":"Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch. It is challenging due to the significant variations inside the target scenario, e.g., different camera viewpoint, illumination changes, and occlusion. These variations result in a gap between the distribution of each mini-batch and the distribution of the whole dataset when using mini-batch training. In this paper, we study model fine-tuning from the perspective of the aggregation and utilization of the global information of the dataset when using mini-batch training. Specifically, we introduce a novel network structure called Batch-related Convolutional Cell (BConv-Cell), which progressively collects the global information of the dataset into a latent state and uses this latent state to rectify the extracted feature. Based on BConv-Cells, we further proposed the Progressive Transfer Learning (PTL) method to facilitate the model fine-tuning process by joint training the BConv-Cells and the pre-trained ReID model. Empirical experiments show that our proposal can improve the performance of the ReID model greatly on MSMT17, Market-1501, CUHK03 and DukeMTMC-reID datasets. The code will be released later on at \\url{https://github.com/ZJULearning/PTL}","doi":"10.24963/ijcai.2019/586","url":"https://ijcai.org/proceedings/2019/586","pdf":"https://ijcai.org/proceedings/2019/0586.pdf","markdown":"https://ijcai.org/proceedings/2019/586.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/586","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":"Machine Learning M-Z","track":"Main track","number":586,"pages":{"first":4220,"last":4226},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[],"references":[{"position":1,"raw":"[Deng et al., 2018] Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, and Jianbin Jiao. Image- image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 994-1003, 2018.","title":"Imageimage domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification","authors":"Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, Jianbin Jiao","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Fan et al., 2018] Hehe Fan, Liang Zheng, Chenggang Yan, and Yi Yang. Unsupervised person re-identification: Clus- tering and fine-tuning. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 14(4):83, 2018.","title":"Unsupervised person re-identification: Clustering and fine-tuning","authors":"Fan","year":"2018","venue":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Finn et al., 2017] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1126- 1135. JMLR. org, 2017.","title":"Model-agnostic meta-learning for fast adaptation of deep networks","authors":"Finn","year":"2017","venue":"Proceedings of the 34th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Geng et al., 2016] Mengyue Geng, Yaowei Wang, Tao Xi- ang, and Yonghong Tian. Deep transfer learning for person re-identification. arXiv preprint arXiv:1611.05244, 2016.","title":"Deep transfer learning for person re-identification","authors":"Geng","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ha et al., 2016] David Ha, Andrew M. Dai, and Quoc V. Le. Hypernetworks. CoRR, abs/1609.09106, 2016.","title":null,"authors":"David Ha, Andrew M Dai, V Quoc, Le, Hypernetworks","year":"2016","venue":null,"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 Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hinton et al., 2015] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.","title":"Distilling the knowledge in a neural network","authors":"Geoffrey Hinton, Oriol Vinyals, Jeff Dean","year":"2015","venue":null,"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 connected convolutional networks. In CVPR, volume 1, page 3, 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":"[Keskar et al., 2016] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. CoRR, abs/1609.04836, 2016.","title":"On large-batch training for deep learning: Generalization gap and sharp minima","authors":"Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak, Peter Tang","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li et al., 2014] Wei Li, Rui Zhao, Tong Xiao, and Xiao- gang Wang. Deepreid: Deep filter pairing neural network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 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":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Li et al., 2018] Wei Li, Xiatian Zhu, and Shaogang Gong. Harmonious attention network for person re-identification. In CVPR, volume 1, page 2, 2018.","title":"Harmonious attention network for person re-identification","authors":"Wei Li, Xiatian Zhu, Shaogang Gong","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ma et al., 2018] Liqian Ma, Qianru Sun, Stamatios Geor- goulis, Luc Van Gool, Bernt Schiele, and Mario Fritz. Dis- entangled person image generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, pages 99-108, 2018.","title":"Liqian Ma, Qianru Sun, Stamatios Georgoulis","authors":"Ma","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":13,"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, Alexander C. Berg, and Li Fei-Fei. Ima- geNet Large Scale Visual Recognition Challenge. Inter- national Journal of Computer Vision (IJCV), 115(3):211- 252, 2015.","title":"Ima-geNet 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, Alexander C Berg, Li Fei-Fei","year":"2015","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Sun et al., 2018] Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional base- line). In Proceedings of the European Conference on Com- puter Vision (ECCV), pages 480-496, 2018.","title":"Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)","authors":"Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, Shengjin Wang","year":"2018","venue":"Proceedings of the European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Varior et al., 2016] Rahul Rama Varior, Mrinal Haloi, and Gang Wang. Gated siamese convolutional neural net- work architecture for human re-identification. In European Conference on Computer Vision, pages 791-808. Springer, 2016.","title":"Gated siamese convolutional neural network architecture for human re-identification","authors":"Rahul Rama Varior, Mrinal Haloi, Gang Wang","year":"2016","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Wang et al., 2018] Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, and Xi Zhou. Learning discrimina- tive features with multiple granularities for person re- identification. In 2018 ACM Multimedia Conference on Multimedia Conference, pages 274-282. ACM, 2018.","title":"Learning discriminative features with multiple granularities for person reidentification","authors":"Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, Xi Zhou","year":"2018","venue":"ACM Multimedia Conference on Multimedia Conference","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wei et al., 2018] Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. Person trasfer gan to bridge domain gap for person re-identification. In Computer Vision and Pattern Recognition, IEEE Conference on, 2018.","title":"Person trasfer gan to bridge domain gap for person re-identification","authors":"Wei","year":"2018","venue":"Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Wu, 2016] Xundong Wu. High performance binarized neu- ral networks trained on the imagenet classification task. CoRR, abs/1604.03058, 2016.","title":"High performance binarized neural networks trained on the imagenet classification task","authors":"Xundong Wu","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Xingjian et al., 2015] SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional lstm network: A machine learning ap- proach for precipitation nowcasting. In Advances in neural information processing systems, pages 802-810, 2015.","title":"Convolutional lstm network: A machine learning approach for precipitation nowcasting","authors":"Xingjian","year":"2015","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Zhang et al., 2017] Xuan Zhang, Hao Luo, Xing Fan, Weilai Xiang, Yixiao Sun, Qiqi Xiao, Wei Jiang, Chi Zhang, and Jian Sun. Alignedreid: Surpassing human- level performance in person re-identification. arXiv preprint arXiv:1711.08184, 2017.","title":"Alignedreid: Surpassing humanlevel performance in person re-identification","authors":"Xuan Zhang, Hao Luo, Xing Fan, Weilai Xiang, Yixiao Sun, Qiqi Xiao, Wei Jiang, Chi Zhang, Jian Sun","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhang et al., 2018] Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu. Deep mutual learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4320-4328, 2018.","title":"Deep mutual learning","authors":"Ying Zhang, Tao Xiang, Timothy M Hospedales, Huchuan Lu","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":22,"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 Proceedings of the IEEE International Conference on Computer Vision, 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":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zheng et al., 2017] Zhedong Zheng, Liang Zheng, and Yi Yang. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In Proceed- ings of the IEEE International Conference on Computer Vision, 2017.","title":"Unlabeled samples generated by gan improve the person re-identification baseline in vitro","authors":"Zheng","year":"2017","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhong et al., 2018] Zhun Zhong, Liang Zheng, Zhedong Zheng, Shaozi Li, and Yi Yang. Camera style adaptation for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5157-5166, 2018.","title":"Camera style adaptation for person re-identification","authors":"Zhun Zhong, Liang Zheng, Zhedong Zheng, Shaozi Li, Yi Yang","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}