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In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach.
R. Layne, T. M. Hospedales, and S. Gong, “Person Re-identification by Attributes,” in British Machine Vision Conference , 2012
2012
Earlier work this paper cites.
J. Zhu, S. Liao, Z. Lei, D. Yi, and S. Z. Li, “Pedestrian Attribute Classification in Surveillance: Database and Evaluation,” in IEEE International Conference on Computer Vision Workshops , 2013, pp. 331–338
2013
Earlier work this paper cites.
S. Ji, W. Xu, M. Yang, and K. Yu, “3d convolutional neural networks for human action recognition,” IEEE transactions on pattern analysis and machine intelligence , 2013
2013
Earlier work this paper cites.
Y. Deng, P. Luo, C. C. Loy, and X. Tang, “Pedestrian Attribute Recognition At Far Distance,” in ACM International Conference on Multimedia , 2014, pp. 789–792
2014
Earlier work this paper cites.
N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev, “Panda: Pose aligned networks for deep attribute modeling,” in IEEE Conference on Computer Vision and Pattern Recognition , 2014
2014
Earlier work this paper cites.
Y. Tian, P. Luo, X. Wang, and X. Tang, “Pedestrian Detection aided by Deep Learning Semantic Tasks,” in IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 5079–5087
2015
Earlier work this paper cites.
A. Li, L. Liu, K. Wang, S. Liu, and S. Yan, “Clothing Attributes Assisted Person Re-identification,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 25, no. 5, pp. 869–878, 2015
2015
Earlier work this paper cites.
J. Zhu, S. Liao, D. Yi, Z. Lei, and S. Z. Li, “Multi-label CNN Based Pedestrian Attribute Learning for Soft Biometrics,” in International Conference on Biometrics . IEEE, 2015, pp. 535–540
2015
Earlier work this paper cites.
P. Sudowe, H. Spitzer, and B. Leibe, “Person Attribute Recognition with a Jointly-Trained Holistic CNN Model,” in IEEE International Conference on Computer Vision Workshops , 2015, pp. 329–337
2015
Earlier work this paper cites.
D. Li, X. Chen, and K. Huang, “Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios,” in Asian Conference on Pattern Recognition , 2015, pp. 111–115
2015
Earlier work this paper cites.
L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, “Scalable Person Re-identification: A Benchmark,” in IEEE International Conference on Computer Vision , 2015
2015
Cited alongside, same era.
T. Matsukawa and E. Suzuki, “Person Re-Identification Using CNN Features Learned from Combination of Attributes,” in International Conference on Pattern Recognition , 2016, pp. 2428–2433
2016
Cited alongside, same era.
2016
Cited alongside, same era.
L. Zheng, Z. Bie, Y. Sun, J. Wang, C. Su, S. Wang, and Q. Tian, “MARS: A Video Benchmark for Large-Scale Person Re-identification,” in European Conference on Computer Vision , 2016
2016
Cited alongside, same era.
2017
Later among the works it cites.
C. Su, F. Yang, S. Zhang, Q. Tian, L. S. Davis, and W. Gao, “Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 5, pp. 1167–1181, 2018
2018
Later among the works it cites.
J. Wang, X. Zhu, S. Gong, and W. Li, “Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2275–2284
2018
Later among the works it cites.
X. Chang, T. M. Hospedales, and T. Xiang, “Multi-level factorisation net for person re-identification,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018
2018
Later among the works it cites.
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
2016
Cited alongside, same era.
X. Liu, H. Zhao, M. Tian, L. Sheng, J. Shao, S. Yi, J. Yan, and X. Wang, “HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis,” in IEEE International Conference on Computer Vision , 2017, pp. 350–359
2017
Cited alongside, same era.
J. Wang, X. Zhu, S. Gong, and W. Li, “Attribute Recognition by Joint Recurrent Learning of Context and Correlation,” in IEEE International Conference on Computer Vision , 2017, pp. 531–540
2017
Cited alongside, same era.
M. S. Sarfraz, A. Schumann, Y. Wang, and R. Stiefelhagen, “Deep view-sensitive pedestrian attribute inference in an end-to-end model,” 2017
2017
Cited alongside, same era.
K. He, Z. Wang, Y. Fu, R. Feng, Y.-G. Jiang, and X. Xue, “Adaptively weighted multi-task deep network for person attribute classification,” in ACM International Conference on Multimedia , 2017
2017
Cited alongside, same era.
X. Zhao, L. Sang, G. Ding, Y. Guo, and X. Jin, “Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning,” in International Joint Conference on Artificial Intelligence , 2018, pp. 3177–3183
2018
Later among the works it cites.
D. Li, X. Chen, Z. Zhang, and K. Huang, “Pose Guided Deep Model for Pedestrian Attribute Recognition in Surveillance Scenarios,” in IEEE International Conference on Multimedia and Expo , 2018, pp. 1–6
2018
Later among the works it cites.
H. U. Cheng, L. Chen, X. Zhang, and S. Y. Sun, “Pedestrian attribute recognition based on convolutional neural network in surveillance scenarios,” Modern Computer , 2018
2018
Later among the works it cites.
C. Sun, N. Jiang, L. Zhang, Y. Wang, W. Wu, and Z. Zhou, “Unified framework for joint attribute classification and person re-identification,” in International Conference on Artificial Neural Networks . Springer, 2018, pp. 637–647
2018
Later among the works it cites.
Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang, “Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018
2018
Later among the works it cites.