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Cheng, F. Tao, Y. Zhan, M. Li, and K. Li, \u201cHierarchical attributes learning for pedestrian re-identification via parallel stochastic gradient descent combined with momentum correction and adaptive learning rate,\u201d Neural Computing and Applications, vol.32, no.10, pp.5695-5712, 2020. 10.1007\/s00521-019-04485-2","DOI":"10.1007\/s00521-019-04485-2"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] H. Galiyawala and M.S. Raval, \u201cPerson retrieval in surveillance using textual query: a review,\u201d Multimedia Tools and Applications, vol.80, no.18, pp.27343-27383, 2021. 10.1007\/s11042-021-10983-0","DOI":"10.1007\/s11042-021-10983-0"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] Z. Ji and S. Li, \u201cMultimodal alignment and attention-based person search via natural language description,\u201d IEEE Internet Things J., vol.7, no.11, pp.11147-11156, 2020. 10.1109\/jiot.2020.2995148","DOI":"10.1109\/JIOT.2020.2995148"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] Y. Lin, L. Zheng, Z. Zheng, Y. Wu, Z. Hu, C. Yan, and Y. Yang, \u201cImproving person re-identification by attribute and identity learning,\u201d Pattern recognition, vol.95, pp.151-161, 2019. 10.1016\/j.patcog.2019.06.006","DOI":"10.1016\/j.patcog.2019.06.006"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] D. Wu, S.-J. Zheng, X.-P. Zhang, C.-A. Yuan, F. Cheng, Y. Zhao, Y.-J. Lin, Z.-Q. Zhao, Y.-L. Jiang, and D.-S. Huang, \u201cDeep learning-based methods for person re-identification: A comprehensive review,\u201d Neurocomputing, vol.337, pp.354-371, 2019. 10.1016\/j.neucom.2019.01.079","DOI":"10.1016\/j.neucom.2019.01.079"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proc. IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] D. Li, X. Chen, Z. Zhang, and K. Huang, \u201cPose guided deep model for pedestrian attribute recognition in surveillance scenarios,\u201d 2018 IEEE international conference on multimedia and expo (ICME), pp.1-6, IEEE, 2018. 10.1109\/icme.2018.8486604","DOI":"10.1109\/ICME.2018.8486604"},{"key":"8","unstructured":"[8] P. Liu, X. Liu, J. Yan, and J. Shao, \u201cLocalization Guided Learning for Pedestrian Attribute Recognition,\u201d British Machine Vision Conference, 2018."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] X. Liu, H. Zhao, M. Tian, L. Sheng, J. Shao, S. Yi, J. Yan, and X. Wang, \u201cHydraplus-net: Attentive deep features for pedestrian analysis,\u201d Proc. IEEE international conference on computer vision, pp.350-359, 2017. 10.1109\/iccv.2017.46","DOI":"10.1109\/ICCV.2017.46"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] G. Gkioxari, R. Girshick, and J. Malik, \u201cActions and attributes from wholes and parts,\u201d Proc. IEEE international conference on computer vision, pp.2470-2478, 2015. 10.1109\/iccv.2015.284","DOI":"10.1109\/ICCV.2015.284"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] C. Tang, L. Sheng, Z.-X. Zhang, and X. Hu, \u201cImproving pedestrian attribute recognition with weakly-supervised multi-scale attribute-specific localization,\u201d Proc. IEEE\/CVF International Conference on Computer Vision, pp.4997-5006, 2019. 10.1109\/iccv.2019.00510","DOI":"10.1109\/ICCV.2019.00510"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] L. Yang, L. Zhu, Y. Wei, S. Liang, and P. Tan, \u201cAttribute recognition from adaptive parts,\u201d arXiv preprint arXiv:1607.01437, 2016.","DOI":"10.5244\/C.30.81"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] N. Zhang, M. Paluri, M.A. Ranzato, T. Darrell, and L. Bourdev,\u201cPanda: Pose aligned networks for deep attribute modeling,\u201d Proc. IEEE conference on computer vision and pattern recognition, pp.1637-1644, 2014. 10.1109\/cvpr.2014.212","DOI":"10.1109\/CVPR.2014.212"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Y. Liu, M. Tian, J. Hou, S. Yi, and Z. Lin, \u201cPentadent-net: Pedestrian attribute recognition with distance refinement and correlation mining,\u201d 2020 IEEE International Conference on Image Processing (ICIP), pp.2211-2215, IEEE, 2020. 10.1109\/icip40778.2020.9190783","DOI":"10.1109\/ICIP40778.2020.9190783"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] X. Zhao, L. Sang, G. Ding, Y. Guo, and X. Jin, \u201cGrouping attribute recognition for pedestrian with joint recurrent learning,\u201d IJCAI, vol.2018, pp.3177-3183, 2018. 10.24963\/ijcai.2018\/441","DOI":"10.24963\/ijcai.2018\/441"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] X. Zhao, L. Sang, G. Ding, J. Han, N. Di, and C. Yan, \u201cRecurrent attention model for pedestrian attribute recognition,\u201d Proc. AAAI Conference on Artificial Intelligence, vol.33, no.1, pp.9275-9282, 2019. 10.1609\/aaai.v33i01.33019275","DOI":"10.1609\/aaai.v33i01.33019275"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] X. Song, H. Yang, and C. Zhou, \u201cPedestrian attribute recognition with graph convolutional network in surveillance scenarios,\u201d Future Internet, vol.11, no.11, p.245, 2019. 10.3390\/fi11110245","DOI":"10.3390\/fi11110245"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Q. Li, X. Zhao, R. He, and K. Huang, \u201cPedestrian Attribute Recognition by Joint Visual-semantic Reasoning and Knowledge Distillation,\u201d IJCAI, pp.833-839, 2019. 10.24963\/ijcai.2019\/117","DOI":"10.24963\/ijcai.2019\/117"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] Q. Li, X. Zhao, R. He, and K. Huang, \u201cVisual-semantic graph reasoning for pedestrian attribute recognition,\u201d Proc. AAAI conference on artificial intelligence, vol.33, no.1, pp.8634-8641, 2019. 10.1609\/aaai.v33i01.33018634","DOI":"10.1609\/aaai.v33i01.33018634"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] Z. Tan, Y. Yang, J. Wan, G. Guo, and S.Z. Li, \u201cRelation-aware pedestrian attribute recognition with graph convolutional networks,\u201d Proc. AAAI conference on artificial intelligence, vol.34, no.7, pp.12055-12062, 2020. 10.1609\/aaai.v34i07.6883","DOI":"10.1609\/aaai.v34i07.6883"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] M. Fabbri, S. Calderara, and R. Cucchiara, \u201cGenerative adversarial models for people attribute recognition in surveillance,\u201d 2017 14th IEEE international conference on advanced video and signal based surveillance (AVSS), pp.1-6, IEEE, 2017. 10.1109\/avss.2017.8078521","DOI":"10.1109\/AVSS.2017.8078521"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] S. Park, B.X. Nie, and S.-C. Zhu, \u201cAttribute and-or grammar for joint parsing of human pose, parts and attributes,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.40, no.7, pp.1555-1569, 2017. 10.1109\/tpami.2017.2731842","DOI":"10.1109\/TPAMI.2017.2731842"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] M. Wu, D. Huang, Y. Guo, and Y. Wang, \u201cDistraction-aware feature learning for human attribute recognition via coarse-to-fine attention mechanism,\u201d Proc. AAAI conference on artificial intelligence, vol.34, no.7, pp.12394-12401, 2020. 10.1609\/aaai.v34i07.6925","DOI":"10.1609\/aaai.v34i07.6925"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] N. Sarafianos, X. Xu, and I.A. Kakadiaris, \u201cDeep imbalanced attribute classification using visual attention aggregation,\u201d Proc. European Conference on Computer Vision (ECCV), pp.708-725, 2018. 10.1007\/978-3-030-01252-6_42","DOI":"10.1007\/978-3-030-01252-6_42"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] H. Zeng, H. Ai, Z. Zhuang, and L. Chen, \u201cMulti-task learning via co-attentive sharing for pedestrian attribute recognition,\u201d 2020 IEEE International Conference on Multimedia and Expo (ICME), pp.1-6, IEEE, 2020. 10.1109\/icme46284.2020.9102757","DOI":"10.1109\/ICME46284.2020.9102757"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] J. Wang, X. Zhu, S. Gong, and W. Li, \u201cAttribute recognition by joint recurrent learning of context and correlation,\u201d Proc. IEEE International Conference on Computer Vision, pp.531-540, 2017. 10.1109\/iccv.2017.65","DOI":"10.1109\/ICCV.2017.65"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] M. Gori, G. Monfardini, and F. Scarselli, \u201cA new model for learning in graph domains,\u201d Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., vol.2, pp.729-734, IEEE, 2005. 10.1109\/ijcnn.2005.1555942","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"28","unstructured":"[28] Y. Li, D. Tarlow, M. Brockschmidt, and R.S. Zemel, \u201cGated graph sequence neural networks,\u201d CoRR, vol.abs\/1511.05493, 2015."},{"key":"29","unstructured":"[29] P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, \u201cGraph attention networks,\u201d stat, vol.1050, no.20, p.10.48550, 2017."},{"key":"30","unstructured":"[30] T.N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d arXiv preprint arXiv:1609.02907, 2016."},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] Y. Tai, J. Yang, and X. Liu, \u201cImage super-resolution via deep recursive residual network,\u201d Proc. IEEE conference on computer vision and pattern recognition, pp.3147-3155, 2017. 10.1109\/cvpr.2017.298","DOI":"10.1109\/CVPR.2017.298"},{"key":"32","unstructured":"[32] S. Ioffe and C. Szegedy, \u201cBatch normalization: Accelerating deep network training by reducing internal covariate shift,\u201d International Conference on Machine Learning, pp.448-456, pmlr, 2015."},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] Y. Deng, P. Luo, C.C. Loy, and X. Tang, \u201cPedestrian attribute recognition at far distance,\u201d Proc. 22nd ACM International Conference on Multimedia, pp.789-792, 2014.","DOI":"10.1145\/2647868.2654966"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] D. Li, Z. Zhang, X. Chen, and K. Huang, \u201cA Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios,\u201d IEEE Trans. Image Process., vol.28, no.4, pp.1575-1590, 2019. 10.1109\/tip.2018.2878349","DOI":"10.1109\/TIP.2018.2878349"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] M.-L. Zhang and Z.-H. Zhou, \u201cA review on multi-label learning algorithms,\u201d IEEE Trans. Knowl. Data Eng., vol.26, no.8, pp.1819-1837, 2014. 10.1109\/tkde.2013.39","DOI":"10.1109\/TKDE.2013.39"},{"key":"36","unstructured":"[36] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980, 2014."},{"key":"37","unstructured":"[37] M.S. Sarfraz, A. Schumann, Y. Wang, and R. Stiefelhagen, \u201cDeep view-sensitive pedestrian attribute inference in an end-to-end Model,\u201d arXiv, vol.abs\/1707.06089, 2017."},{"key":"38","doi-asserted-by":"publisher","unstructured":"[38] Z. Tan, Y. Yang, J. Wan, H. Hang, G. Guo, and S.Z. Li, \u201cAttention-based pedestrian attribute analysis,\u201d IEEE Trans. Image Process., vol.28, no.12, pp.6126-6140, 2019. 10.1109\/tip.2019.2919199","DOI":"10.1109\/TIP.2019.2919199"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] K. Han, Y. Wang, H. Shu, C. Liu, C. Xu, and C. Xu, \u201cAttribute aware pooling for pedestrian attribute recognition,\u201d arXiv preprint arXiv:1907.11837, 2019.","DOI":"10.24963\/ijcai.2019\/341"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[40] Y. Yang, Z. Tan, P. Tiwari, H.M. Pandey, J. Wan, Z. Lei, G. Guo, and S.Z. Li, \u201cCascaded Split-and-Aggregate Learning with Feature Recombination for Pedestrian Attribute Recognition,\u201d International Journal of Computer Vision, vol.129, no.10, pp.2731-2744, 2021. 10.1007\/s11263-021-01499-z","DOI":"10.1007\/s11263-021-01499-z"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] J. Jia, X. Chen, and K. Huang, \u201cSpatial and semantic consistency regularizations for pedestrian attribute recognition,\u201d Proc. IEEE\/CVF international conference on computer vision, pp.962-971, 2021. 10.1109\/iccv48922.2021.00100","DOI":"10.1109\/ICCV48922.2021.00100"},{"key":"42","doi-asserted-by":"publisher","unstructured":"[42] L. Chen, J. Song, X. Zhang, and M. Shang, \u201cMCFL: multi-label contrastive focal loss for deep imbalanced pedestrian attribute recognition,\u201d Neural Computing and Applications, vol.34, no.19, pp.16701-16715, 2022. 10.1007\/s00521-022-07300-7","DOI":"10.1007\/s00521-022-07300-7"},{"key":"43","doi-asserted-by":"publisher","unstructured":"[43] H. Fan, H.-M. Hu, S. Liu, W. Lu, and S. Pu, \u201cCorrelation Graph Convolutional Network for Pedestrian Attribute Recognition,\u201d IEEE Trans. Multimedia, vol.24, pp.49-60, 2022. 10.1109\/tmm.2020.3045286","DOI":"10.1109\/TMM.2020.3045286"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[44] W. Li, Z. Cao, J. Feng, J. Zhou, and J. Lu, \u201cLabel2label: A language modeling framework for multi-attribute learning,\u201d European Conference on Computer Vision, pp.562-579, Springer, 2022. 10.1007\/978-3-031-19775-8_33","DOI":"10.1007\/978-3-031-19775-8_33"},{"key":"45","doi-asserted-by":"publisher","unstructured":"[45] J. Jia, N. Gao, F. He, X. Chen, and K. Huang, \u201cLearning disentangled attribute representations for robust pedestrian attribute recognition,\u201d Proc. AAAI Conference on Artificial Intelligence, vol.36, no.1, pp.1069-1077, 2022. 10.1609\/aaai.v36i1.19991","DOI":"10.1609\/aaai.v36i1.19991"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] Y. Ci, Y. Wang, M. Chen, S. Tang, L. Bai, F. Zhu, R. Zhao, F. Yu, D. Qi, and W. Ouyang, \u201cUniHCP: A Unified Model for Human-Centric Perceptions,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.17840-17852, 2023. 10.1109\/cvpr52729.2023.01711","DOI":"10.1109\/CVPR52729.2023.01711"},{"key":"47","doi-asserted-by":"publisher","unstructured":"[47] Z. Liu, Z. Zhang, D. Li, P. Zhang, and C. Shan, \u201cDual-branch self-attention network for pedestrian attribute recognition,\u201d Pattern Recognition Letters, vol.163, pp.112-120, 2022. 10.1016\/j.patrec.2022.10.003","DOI":"10.1016\/j.patrec.2022.10.003"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/3\/E107.D_2023EDP7134\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T16:45:23Z","timestamp":1731516323000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/3\/E107.D_2023EDP7134\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":47,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023edp7134","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2024,3,1]]},"article-number":"2023EDP7134"}}