T. Xiao, S Li, B. Wang, L. Lin, X. Wang, Joint detection and identification feature learning for person search, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 3376–3385 (2017)
S. Zhang, R. Benenson, B. Schiele, Citypersons: A diverse dataset for pedestrian detection, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 4457–4465 (2017)
K. Zheng, W. Liu, L. He, T. Mei, J. Luo, Z.-J. Zha, Group-aware label transfer for domain adaptive person re-identification, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5306–5315 (2021)
B. Munjal, S. Amin, F. Tombari, F. Galasso, Query-guided end-to-end person search, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 811–820 (2019)
D. Deif, Y. Gadallah, A comprehensive wireless sensor network reliability metric for critical internet of things applications. EURASIP J. Wirel. Commun. Netw. 145 (2017)
F. Zhao, X. Sun, H. Chen, R. Bie, Outage performance of relay-assisted primary and secondary transmissions in cognitive relay networks. EURASIP J. Wirel. Commun. Netw. 60 (2014)
Y. Yan, J. Li, J. Qin, S. Bai, S. Liao, L. Liu, F. Zhu, L. Shao, Anchor-free person search, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7686–7695 (2021)
W. Liu, S. Liao, W. Ren, W. Hu, Y. Yu, High-level semantic feature detection: a new perspective for pedestrian detection, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5182–5191 (2019)
S. Zhang, D. Chen, J. Yang, B. Schiele, Guided attention in CNNs for occluded pedestrian detection and re-identification. Int. J. Computer Vis. (4) (2021)
S. Woo, J. Park, J.-Y. Lee, I.S. Kweon, CBAM: Convolutional block attention module, in European Conference on Computer Vision, pp. 3–19 (2018)
Y. Xu, B. Ma, R. Huang, L. Lin, Person search in a scene by jointly modeling peoplecommonness and person uniqueness, in The ACM International Conference (2014)
L. Zheng, H. Zhang, S. Sun, M. Chandraker, Y. Yang, Q. Tian, Person re-identification in the wild, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 3346–3355 (2017)
H. Liu, W. Shi, W. Huang, Q. Guan, A discriminatively learned feature embedding based on multi-loss fusion for person search, in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1668–1672 (2018)
W. Shi, H. Liu, F. Meng, W. Huang, Instance enhancing loss: Deep identity-sensitive feature embedding for person search, in IEEE International Conference on Image Processing, pp. 4108–4112 (2018)
J. Xiao, Y. Xie, T. Tillo, K. Huang, Y. Wei, J. Feng, IAN: the individual aggregation network for person search. Pattern Recogn. 87, 332–340 (2019)
H. Liu, J. Feng, Z. Jie, K. Jayashree, B. Zhao, M. Qi, J. Jiang, S. Yan, Neural person search machines, in IEEE International Conference on Computer Vision, pp. 493–501 (2017)
X. Chang, P.-Y. Huang, Y.-D. Shen, X. Liang, Y. Yang, A.G. Hauptmann, RCAA: Relational context-aware agents for person search, in European Conference on Computer Vision, pp. 86–102 (2018)
X. Lan, X. Zhu, S. Gong, Person search by multi-scale matching, in European Conference on Computer Vision, pp. 553–569 (2018)
P. Dollár, Z. Tu, P. Perona, S. Belongie, Integral channel features, in British Machine Vision Conference, BMVC 2009, London, UK, September 7–10, 2009. Proceedings (2009)
P. Dollár, R. Appel, S. Belongie, P. Perona, Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)
P.F. Felzenszwalb, R.B. Girshick, D. McAllester, D. Ramanan, Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
W. Ouyang, X. Wang, A discriminative deep model for pedestrian detection with occlusion handling, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 3258–3265 (2012)
Y. Tian, P. Luo, X. Wang, X. Tang, Deep learning strong parts for pedestrian detection, in IEEE International Conference on Computer Vision, pp. 1904–1912 (2015)
L. Zhang, L. Lin, X. Liang, K. He, Is faster R-CNN doing well for pedestrian detection, in European Conference on Computer Vision, pp. 443–457 (2016)
J. Li, X. Liang, S. Shen, T. Xu, J. Feng, S. Yan, Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimedia 20(4), 985–996 (2018)
S. Zhang, L. Wen, X. Bian, Z. Lei, S.Z. Li, Occlusion-aware R-CNN: detecting pedestrians in a crowd, in European Conference on Computer Vision, pp. 657–674 (2018)
Z. Cai, M. Saberian, N. Vasconcelos, Learning complexity-aware cascades for deep pedestrian detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(9), 2195–2211 (2020)
T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollár, Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318–327 (2020)
X. Du, M. El-Khamy, J. Lee, L. Davis, Fused DNN: a deep neural network fusion approach to fast and robust pedestrian detection, in IEEE Winter Conference on Applications of Computer Vision, pp. 953–961 (2017)
S. Zhang, J. Yang, B. Schiele, Occluded pedestrian detection through guided attention in CNNs, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6995–7003 (2018)
J. Xu, R. Zhao, F. Zhu, H. Wang, W. Ouyang, Attention-aware compositional network for person re-identification, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2119–2128 (2018)
H. Liu, J. Feng, M. Qi, J. Jiang, S. Yan, End-to-end comparative attention networks for person re-identification. IEEE Trans. Image Process. 26(7), 3492–3506 (2017)
S. Ding, L. Lin, G. Wang, H. Chao, Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48(10), 2993–3003 (2015)
X. Fan, W. Jiang, H. Luo, M. Fei, Spherereid:deep hypersphere manifold embedding for person re-identification. J. Vis. Commun. Image Represent. 60, 51–58 (2019)
W. Xiang, J. Huang, X. Qi, X. Hua, L. Zhang, Homocentric hypersphere feature embedding for person re-identification, in IEEE International Conference on Image Processing, pp. 1237–1241 (2019)
D. Cheng, Y. Gong, S. Zhou, J. Wang, N. Zheng, Person re-identification by multi-channel parts-based CNN with improved triplet loss function, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1335–1344 (2016)
T. Xiao, H. Li, x W. Li, X. Wang, Learning deep feature representations with domain guided dropout for person re-identification, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1249–1258 (2016)
C. Han, J. Ye, Y. Zhong, X. Tan, C. Zhang, C. Gao, N. Sang, Re-id driven localization refinement for person search, in IEEE/CVF International Conference on Computer Vision, pp. 9813–9822 (2019)
Z. Guo, D. Han, Sparse co-attention visual question answering networks based on thresholds. Appl Intell. (2022). https://doi.org/10.1007/s10489-022-03559-4
C. Chen, D. Han, C.-C. Chang, CAAN: Context-Aware attention network for visual question answering. Pattern Recognit. (2022). https://doi.org/10.1016/j.patcog.2022.108980
C. Su, J. Li, S. Zhang, J. Xing, W. Gao, Q. Tian, Pose-driven deep convolutional model for person re-identification, in IEEE International Conference on Computer Vision, pp. 3980–3989 (2017)
X. Liu, H. Zhao, M. Tian, L. Sheng, J. Shao, S. Yi, J. Yan, X. Wang, Hydraplus-net:attentive deep features for pedestrian analysis, in IEEE International Conference on Computer Vision, pp. 350–359 (2017)
Z. Tian, C. Shen, H. Chen, T. He, FCOS: Fully convolutional one-stage object detection, in IEEE/CVF International Conference on Computer Vision, pp. 9626–9635 (2019)
T. Zhao, X. Wu, Pyramid feature attention network for saliency detection, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3080–3089 (2019)
Y. Lu, Z. Hong, B. Liu, W. Li, N. Yu, DHFF: Robust multi-scale person search by dynamic hierarchical feature fusion, in IEEE International Conference on Image Processing, pp. 3935–3939 (2019)
W. Dong, Z. Zhang, C. Song, T. Tan, Bi-directional interaction network for person search, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2836–2845 (2020)
D. Chen, S. Zhang, J. Yang, B. Schiele, Norm-aware embedding for efficient person search, in IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12612–12621 (2020)
H. Yao, C. Xu, Joint person objectness and repulsion for person search. IEEE Trans. Image Process. 30, 685–696 (2021)
W. Liang, J. Long, K.-C. Li, J. Xu, N. Ma, X. Lei, A fast defogging image recognition algorithm based on bilateral hybrid filtering. ACM Trans. Multimedia Comput Commun Appl. 17(42), 1–16 (2021)
M. Cui, D. Han, J. Wang, An Efficient and safe road condition monitoring authentication scheme based on fog computing. IEEE Internet Things J. 6(5), 9076–9084 (2019)
M. Cui, D. Han, J. Wang, K.-C. Li, C.-C. Chang, ARFV: An Efficient Shared Data Auditing Scheme Supporting Revocation for Fog-Assisted Vehicular Ad-Hoc Networks. IEEE Trans Vehicular Technol. 69(12), 15815–15827 (2020)
H. Li, D. Han, M. Tang, A privacy-preserving charging scheme for electric vehicles using blockchain and fog computing. IEEE Syst J. 15(3), 3189–3200 (2020)
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