[1] Zheng L, Yang Y. -10-10)[2019-10-30]. https:∥arxiv., org/abs/1610, 02984(2016).
[2] Gong S G, Cristani M, Yan S C et al. Person re-identification[M]. London: Springer London(2014).
[3] Qi M B, Tan S S, Wang Y X et al. Multi-feature subspace and kernel learning for person re-identification[J]. Acta Automatica Sinica, 42, 299-308(2016).
[4] Zajdel W, Zivkovic Z. Krose B J A. Keeping track of humans: have I seen this person before?. [C]∥Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain. New York: IEEE, 2081-2086(2005).
[5] Matsukawa T, Okabe T, Suzuki E et al. Hierarchical Gaussian descriptor for person re-identification. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1363-1372(2016).
[6] Lisanti G, Masi I, Bagdanov A D et al. Person re-identification by iterative re-weighted sparse ranking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1629-1642(2015).
[7] Zhao R, Oyang W, Wang X G. Person re-identification by saliency learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 356-370(2017).
[8] Farenzena M, Bazzani L, Perina A et al. Person re-identification by symmetry-driven accumulation of local features. [C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 2360-2367(2010).
[9] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).
[10] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Advances in Neural Information Processing Systems 25, December 3-6, 2012, Lake Tahoe, Nevada, USA. New York: Neural Information Processing Systems Foundation, Inc., 1097-1105(2012).
[11] Ren S, He K, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks. [C]∥ Advances in Neural Information Processing Systems 28, December 7-12, 2015, Montreal, Canada. New York: Neural Information Processing Systems Foundation, Inc., 91-99(2015).
[12] Wang H, Wang Y T, Zhou Z et al. CosFace: large margin cosine loss for deep face recognition. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 5265-5274(2018).
[13] Goodfellow I, Pouget-Abadie J, MIRZA M et al. Generative adversarial nets. [C]∥ Advances in Neural Information Processing Systems 27, December 8-13, 2014, Montreal, Canada. New York: Neural Information Processing Systems Foundation, Inc., 2672-2680(2014).
[14] Li W, Zhao R, Xiao T et al. DeepReID: deep filter pairing neural network for person re-identification. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 152-159(2014).
[15] Yi D, Lei Z, Liao S C et al. Deep metric learning for person re-identification. [C]∥2014 22nd International Conference on Pattern Recognition, August 24-28, 2014, Stockholm, Sweden. New York: IEEE, 34-39(2014).
[16] Ahmed E, Jones M, Marks T K. An improved deep learning architecture for person re-identification. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 3908-3916(2015).
[17] Zheng L, Zhang H H, Sun S Y et al. Person re-identification in the wild. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 1367-1376(2017).
[18] Zheng L, Bie Z, Sun Y F et al[M]. MARS: A video benchmark for large-scale person Re-identification, 868-884(2016).
[19] Xiao T, Li H S, Ouyang W L et al. Learning deep feature representations with domain guided dropout for person Re-identification. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1249-1258(2016).
[20] Bromley J, Guyon I, Lecun Y et al. Signature verification using a "Siamese" time delay neural network. [C]∥ Advances in Neural Information Processing Systems 6, Denver, Colorado, USA. San Mateo: Morgan Kaufmann Publishers, Inc., 737-744(1993).
[21] Gray D, Tao H. Viewpoint invariant pedestrian recognition with an ensemble of localized features[M]. ∥Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 262-275(2008).
[22] Hirzer M, Beleznai C, Roth P M et al. Person re-identification by descriptive and discriminative classification[M]. ∥Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 91-102(2011).
[23] Li W, Zhao R, Wang X G. Human re-identification with transferred metric learning[M]. ∥Computer Vision-ACCV 2012. Berlin, Heidelberg: Springer Berlin Heidelberg, 31-44(2013).
[24] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).
[25] Xiao T, Li S, Wang B C et al. Joint detection and identification feature learning for person search. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 3415-3424(2017).
[27] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).
[28] Simonyan K. -04-10)[2019-10-30]. https:∥arxiv., org/abs/1409, 1556(2015).
[29] Chen H R, Wang Y W, Shi Y M et al. Deep transfer learning for person re-identification. [C]∥2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), September 13-16, 2018, Xi'an. New York: IEEE, 1-5(2018).
[30] Wang J Y, Zhu X T, Gong S G et al. Transferable joint attribute-identity deep learning for unsupervised person re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 2275-2284(2018).
[31] Li A N, Liu L Q, Wang K et al. Clothing attributes assisted person reidentification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 25, 869-878(2015).
[32] Matsukawa T, Suzuki E. Person re-identification using CNN features learned from combination of attributes. [C]∥2016 23rd International Conference on Pattern Recognition (ICPR), December 4-8, 2016, Cancun. New York: IEEE, 2428-2433(2016).
[33] Lin Y T, Zheng L, Zheng Z D et al. Improving person re-identification by attribute and identity learning[J]. Pattern Recognition, 95, 151-161(2019).
[34] Wu Y C, Chen H C, Li S M et al. Person re-identification using attribute priori distribution[J]. Acta Automatica Sinica, 45, 953-964(2019).
[35] Zheng Z D, Zheng L, Yang Y. A discriminatively learned CNN embedding for person reidentification[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 14, 1-20(2018).
[36] Yang L, Jin R. Distance metric learning: a comprehensive survey[J]. Michigan State Universiy, 1-51(2006).
[37] Varior R R, Haloi M, Wang G[M]. Gated Siamese convolutional neural network architecture for human re-identification, 791-808(2016).
[38] Ding S Y, Lin L, Wang G R et al. Deep feature learning with relative distance comparison for person re-identification[J]. Pattern Recognition, 48, 2993-3003(2015).
[39] Chen W H, Chen X T, Zhang J G et al. Beyond triplet loss: a deep quadruplet network for person re-identification. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 403-412(2017).
[40] Hadsell R, Chopra S. LeCun Y. Dimensionality reduction by learning an invariant mapping. [C]∥2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), New York, NY, USA. New York: IEEE, 1735-1742(2006).
[41] Hermans A, Beyer L. -11-21)[2019-10-30]. https:∥arxiv., org/abs/1703, 07737(2017).
[42] Liu J W, Zha Z J, Tian Q et al. Multi-scale triplet CNN for person re-identification[C]∥Proceedings of the 2016 ACM on Multimedia Conference - MM'16, October 1-19, 2016, Amsterdam, The Netherlands., 192-196(2016).
[43] Cheng D, Gong Y H, Zhou S P et al. Person re-identification by multi-channel parts-based CNN with improved triplet loss function. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1335-1344(2016).
[44] Zhang Z, Si T Z, Liu S. Integration convolutional neural network for person re-identification in camera networks[J]. IEEE Access, 6, 36887-36896(2018).
[45] Wu S X, Chen Y C, Li X et al. An enhanced deep feature representation for person re-identification. [C]∥2016 IEEE Winter Conference on Applications of Computer Vision (WACV), March 7-10, 2016, Lake Placid, NY, USA. New York: IEEE, 1-8(2016).
[46] Yao H T, Zhang S L, Zhang D M et al. Large-scale person re-identification as retrieval. [C]∥2017 IEEE International Conference on Multimedia and Expo (ICME), July 10-14, 2017, Hong Kong, China. New York: IEEE, 1440-1445(2017).
[47] Varior R R, Shuai B, Lu J W et al[M]. A Siamese long short-term memory architecture for human re-identification, 135-153(2016).
[48] Wei shih-en, Ramakrishna V, Kanade T et al. Convolutional pose machines. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 4724-4732(2016).
[49] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 3431-3440(2015).
[50] Insafutdinov E, Pishchulin L, Andres B et al[M]. DeeperCut: a deeper, stronger, and faster multi-person pose estimation model, 34-50(2016).
[51] Cao Z, Simon T, Wei S et al. Realtime multi-person 2D pose estimation using part affinity fields. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 7291-7299(2017).
[52] Zheng L, Huang Y J, Lu H C et al. Pose-invariant embedding for deep person re-identification[J]. IEEE Transactions on Image Processing, 28, 4500-4509(2019).
[53] Zhao H Y, Tian M Q, Sun S Y et al. Spindle net: person re-identification with human body region guided feature decomposition and fusion. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 1077-1085(2017).
[54] Su C, Li J N, Zhang S L et al. Pose-driven deep convolutional model for person re-identification. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice. New York: IEEE, 3960-3969(2017).
[55] Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks. [C]∥ Advances in Neural Information Processing Systems 28, December 7-12, 2015, Montreal, Canada. New York: Neural Information Processing Systems Foundation, Inc., 2017-2025(2015).
[56] Wei L H, Zhang S L, Yao H T et al. GLAD: global-local-alignment descriptor for scalable person re-identification[J]. IEEE Transactions on Multimedia, 21, 986-999(2019).
[57] Sun Y F, Zheng L, Yang Y et al[M]. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline), 501-518(2018).
[58] Zhang X, Luo H, Fan X et al. -01-31)[2019-10-30]. https:∥arxiv., org/abs/1711, 08184(2018).
[60] Xu J, Zhao R, Zhu F et al. Attention-aware compositional network for person re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 2119-2128(2018).
[61] Zhou S P, Wang J J, Meng D Y et al. Discriminative feature learning with foreground attention for person re-identification[J]. IEEE Transactions on Image Processing, 28, 4671-4684(2019).
[62] Li W, Zhu X T, Gong S G. Harmonious attention network for person re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2285-2294(2018).
[63] Zhao L M, Li X, Zhuang Y T et al. Deeply-learned part-aligned representations for person re-identification. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice. New York: IEEE, 3219-3228(2017).
[64] Liu X H, Zhao H Y, Tian M Q et al. HydraPlus-Net: attentive deep features for pedestrian analysis. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice. New York: IEEE, 350-359(2017).
[65] Yang F, Yan K, Lu S J et al. Attention driven person re-identification[J]. Pattern Recognition, 86, 143-155(2019).
[66] Zheng W S, Li X, Xiang T et al. Partial person Re-identification. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 4678-4686(2015).
[67] He L X, Liang J, Li H Q et al. Deep spatial feature reconstruction for partial person re-identification: alignment-free approach. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 7073-7082(2018).
[68] Sun Y F, Xu Q, Li Y L et al. Perceive where to focus: learning visibility-aware part-level features for partial person re-identification. [C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 393-402(2019).
[69] Yu Y, Gong Z Q, Zhong P et al[M]. Unsupervised representation learning with deep convolutional neural network for remote sensing images, 97-108(2017).
[70] Zhu J Y, Park T, Isola P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice. New York: IEEE, 2223-2232(2017).
[71] Chen X, Duan Y, Houthooft R et al. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. [C]∥ Advances in Neural Information Processing Systems 29, December 5-10, 2016, Barcelona, Spain. New York: Neural Information Processing Systems Foundation, Inc., 2172-2180(2016).
[72] Zheng Z D, Zheng L, Yang Y. Unlabeled samples generated by GAN improve the person Re-identification baseline in vitro. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice. New York: IEEE, 3754-3762(2017).
[73] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2818-2826(2016).
[74] Huang Y, Xu J S, Wu Q et al. Multi-pseudo regularized label for generated data in person re-identification[J]. IEEE Transactions on Image Processing, 28, 1391-1403(2019).
[75] Li X, Wu A C, Zheng W S[M]. Adversarial open-world person Re-identification, 287-303(2018).
[76] Liu J X, Ni B B, Yan Y C et al. Pose transferrable person Re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 4099-4108(2018).
[77] Siarohin A, Sangineto E, Lathuiliere S et al. Deformable GANs for pose-based human image generation. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 3408-3416(2018).
[78] Qian X L, Fu Y W, Xiang T et al[M]. Pose-normalized image generation for person Re-identification, 661-678(2018).
[79] Zhong Z, Zheng L, Zheng Z D et al. Camera style adaptation for person Re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 5157-5166(2018).
[80] Sun B, Feng J, Saenko K. Return of frustratingly easy domain adaptation. [C]∥ AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona USA. Palo Alto: AAAI, 2058-2065(2016).
[81] Sun B C, Saenko K[M]. Deep CORAL: correlation alignment for deep domain adaptation, 443-450(2016).
[82] Deng W J, Zheng L, Ye Q X et al. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 994-1003(2018).
[83] Wei L H, Zhang S L, Gao W et al. Person transfer GAN to bridge domain gap for person re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 79-88(2018).
[84] Zhong Z, Zheng L, Li S Z et al[M]. Generalizing a person retrieval model hetero- and homogeneously, 176-192(2018).
[85] Choi Y, Choi M, Kim M et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 8789-8797(2018).
[86] Zheng L, Shen L Y, Tian L et al. Scalable person re-identification: a benchmark. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1116-1124(2015).
[87] Zheng W S, Gong S G, Xiang T. Person re-identification by probabilistic relative distance comparison. [C]∥CVPR 2011, June 20-25, 2011, Colorado Springs, CO, USA. New York: IEEE, 649-656(2011).
[88] Luo H, Gu Y Z, Liao X Y et al. Bag of tricks and a strong baseline for deep person re-identification. [C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 16-17, 2019, Long Beach, CA, USA. New York: IEEE, 4321-4329(2019).
[89] Ye M, Wang Z, Lan X et al. Visible thermal person re-identification via dual-constrained top-ranking. [C]∥ Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), July 13-19, 2018, Stockholm, Sweden. [S. l.]: International Joint Conferences on Artificial, 1092-1099(2018).
