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Text-to-image person re-identification (ReID) aims to search for images containing a person of interest using textual descriptions.
S. Li, T. Xiao, H. Li, B. Zhou, D. Yue, and X. Wang, “Person search with natural language description,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 1970–1979
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W. Li, R. Zhao, T. Xiao, and X. Wang, “Deepreid: Deep filter pairing neural network for person re-identification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2014, pp. 152–159
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B. Klein, G. Lev, G. Sadeh, and L. Wolf, “Associating neural word embeddings with deep image representations using fisher vectors,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015, pp. 4437–4446
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L. Wang, Y. Li, and S. Lazebnik, “Learning deep structure-preserving image-text embeddings,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 5005–5013
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 770–778
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S. Li, T. Xiao, H. Li, W. Yang, and X. Wang, “Identity-aware textual-visual matching with latent co-attention,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 1890–1899
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Z. Niu, M. Zhou, L. Wang, X. Gao, and G. Hua, “Hierarchical multimodal lstm for dense visual-semantic embedding,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 1881–1889
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C. Su, J. Li, S. Zhang, J. Xing, W. Gao, and Q. Tian, “Pose-driven deep convolutional model for person re-identification,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 3960–3969
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H. Zhao, M. Tian, S. Sun, J. Shao, J. Yan, S. Yi, X. Wang, and X. Tang, “Spindle net: Person re-identification with human body region guided feature decomposition and fusion,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 1077–1085
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L. Zhao, X. Li, Y. Zhuang, and J. Wang, “Deeply-learned part-aligned representations for person re-identification,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 3219–3228
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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 Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 350–359
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E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 7167–7176
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N. Sarafianos, X. Xu, and I. A. Kakadiaris, “Deep imbalanced attribute classification using visual attention aggregation,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 680–697
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J. Liu, Z.-J. Zha, R. Hong, M. Wang, and Y. Zhang, “Deep adversarial graph attention convolution network for text-based person search,” in Proc. ACM Int. Conf. Multimedia , 2019, pp. 665–673
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H. Yao, S. Zhang, R. Hong, Y. Zhang, C. Xu, and Q. Tian, “Deep representation learning with part loss for person re-identification,” IEEE Trans. Image Process , vol. 28, no. 6, pp. 2860–2871, 2019
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L. Zheng, Y. Huang, H. Lu, and Y. Yang, “Pose-invariant embedding for deep person re-identification,” IEEE Trans. Image Process , vol. 28, no. 9, pp. 4500–4509, 2019
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T. Chen, C. Xu, and J. Luo, “Improving text-based person search by spatial matching and adaptive threshold,” in Proc. IEEE Winter Conf. Appl. Comout. Vis. , 2018, pp. 1879–1887
2018
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Y. Sun, L. Zheng, Y. Yang, Q. Tian, and S. Wang, “Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline),” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 480–496
2018
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X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 7794–7803
2018
Cited alongside, same era.
L. Wei, S. Zhang, W. Gao, and Q. Tian, “Person transfer gan to bridge domain gap for person re-identification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 79–88
2018
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J. Gu, J. Cai, S. R. Joty, L. Niu, and G. Wang, “Look, imagine and match: Improving textual-visual cross-modal retrieval with generative models,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 7181–7189
2018
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Y. Huang, Q. Wu, C. Song, and L. Wang, “Learning semantic concepts and order for image and sentence matching,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 6163–6171
2018
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K.-H. Lee, X. Chen, G. Hua, H. Hu, and X. He, “Stacked cross attention for image-text matching,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 201–216
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Y. Zhang and H. Lu, “Deep cross-modal projection learning for image-text matching,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 686–701
2018
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J. Guo, Y. Yuan, L. Huang, C. Zhang, J.-G. Yao, and K. Han, “Beyond human parts: Dual part-aligned representations for person re-identification,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 3642–3651
2019
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C.-P. Tay, S. Roy, and K.-H. Yap, “Aanet: Attribute attention network for person re-identifications,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 7134–7143
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T. Ruan, T. Liu, Z. Huang, Y. Wei, S. Wei, and Y. Zhao, “Devil in the details: Towards accurate single and multiple human parsing,” in Proc. AAAI Conf. Artif. Intell. , vol. 33, no. 01, 2019, pp. 4814–4821
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Z. Zhang, C. Lan, W. Zeng, and Z. Chen, “Densely semantically aligned person re-identification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 667–676
2019
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Z. Zhong, L. Zheng, Z. Luo, S. Li, and Y. Yang, “Invariance matters: Exemplar memory for domain adaptive person re-identification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 598–607
2019
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J. Yang, J. Fan, Y. Wang, Y. Wang, W. Gan, L. Liu, and W. Wu, “Hierarchical feature embedding for attribute recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 13 055–13 064
2020
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L. Qu, M. Liu, D. Cao, L. Nie, and Q. Tian, “Context-aware multi-view summarization network for image-text matching,” in Proc. ACM Int. Conf. Multimedia , 2020, pp. 1047–1055
2020
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Y. Jing, C. Si, J. Wang, W. Wang, L. Wang, and T. Tan, “Pose-guided multi-granularity attention network for text-based person search,” in Proc. AAAI Conf. Artif. Intell. , vol. 34, no. 07, 2020, pp. 11 189–11 196
2020
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K. Niu, Y. Huang, W. Ouyang, and L. Wang, “Improving description-based person re-identification by multi-granularity image-text alignments,” IEEE Trans. Image Process , vol. 29, pp. 5542–5556, 2020
2020
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Z. Wang, Z. Fang, J. Wang, and Y. Yang, “Vitaa: Visual-textual attributes alignment in person search by natural language,” in Proc. Eur. Conf. Comput. Vis. , 2020, pp. 402–420
2020
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H. Chen, G. Ding, X. Liu, Z. Lin, J. Liu, and J. Han, “Imram: Iterative matching with recurrent attention memory for cross-modal image-text retrieval,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 12 655–12 663
2020
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Q. Zhang, Z. Lei, Z. Zhang, and S. Z. Li, “Context-aware attention network for image-text retrieval,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 3536–3545
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X. Wei, T. Zhang, Y. Li, Y. Zhang, and F. Wu, “Multi-modality cross attention network for image and sentence matching,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 10 941–10 950
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C. Liu, Z. Mao, T. Zhang, H. Xie, B. Wang, and Y. Zhang, “Graph structured network for image-text matching,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 10 921–10 930
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Z. Zheng, L. Zheng, M. Garrett, Y. Yang, M. Xu, and Y.-D. Shen, “Dual-path convolutional image-text embeddings with instance loss,” Proc. ACM Trans. Multi. Comput. Commun. Appl. , vol. 16, no. 2, pp. 1–23, 2020
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C. Ding, K. Wang, P. Wang, and D. Tao, “Multi-task learning with coarse priors for robust part-aware person re-identification,” IEEE Trans. Pattern Anal. Mach. Intell. , pp. 1–1, 2020
2020
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K. Niu, Y. Huang, and L. Wang, “Textual dependency embedding for person search by language,” in Proc. ACM Int. Conf. Multimedia , 2020, pp. 4032–4040
2020
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Y. Jing, W. Wang, L. Wang, and T. Tan, “Cross-modal cross-domain moment alignment network for person search,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 10 678–10 686
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