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Batch Coherence-Driven Network for Part-Aware Person Re-Identification

Authors:

Changxing Ding,

Dacheng TaoAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 30

Pages 3405 - 3418

https://doi.org/10.1109/TIP.2021.3060909

Published: 01 January 2021 Publication History

Abstract

Existing part-aware person re-identification methods typically employ two separate steps: namely, body part detection and part-level feature extraction. However, part detection introduces an additional computational cost and is inherently challenging for low-quality images. Accordingly, in this work, we propose a simple framework named Batch Coherence-Driven Network (BCD-Net) that bypasses body part detection during both the training and testing phases while still learning semantically aligned part features. Our key observation is that the statistics in a batch of images are stable, and therefore that batch-level constraints are robust. First, we introduce a batch coherence-guided channel attention (BCCA) module that highlights the relevant channels for each respective part from the output of a deep backbone model. We investigate channel-part correspondence using a batch of training images, then impose a novel batch-level supervision signal that helps BCCA to identify part-relevant channels. Second, the mean position of a body part is robust and consequently coherent between batches throughout the training process. Accordingly, we introduce a pair of regularization terms based on the semantic consistency between batches. The first term regularizes the high responses of BCD-Net for each part on one batch in order to constrain it within a predefined area, while the second encourages the aggregate of BCD-Net’s responses for all parts covering the entire human body. The above constraints guide BCD-Net to learn diverse, complementary, and semantically aligned part-level features. Extensive experimental results demonstrate that BCD-Net consistently achieves state-of-the-art performance on four large-scale ReID benchmarks.

References

[1]

E. Ristani and C. Tomasi, “Features for multi-target multi-camera tracking and re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 6036–6046.

[2]

Q. Yang, H.-X. Yu, A. Wu, and W.-S. Zheng, “Patch-based discriminative feature learning for unsupervised person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 3633–3642.

[3]

X. Yang, M. Wang, and D. Tao, “Person re-identification with metric learning using privileged information,” IEEE Trans. Image Process., vol. 27, no. 2, pp. 791–805, Feb. 2018.

[4]

B. Chen, W. Deng, and J. Hu, “Mixed high-order attention network for person re-identification,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 371–381.

[5]

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/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 3641–3650.

[6]

K. Zhou, Y. Yang, A. Cavallaro, and T. Xiang, “Omni-scale feature learning for person re-identification,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 3701–3711.

[7]

Z. Dai, M. Chen, X. Gu, S. Zhu, and P. Tan, “Batch DropBlock network for person re-identification and beyond,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 3690–3700.

[8]

B. Nguyen and B. De Baets, “Kernel distance metric learning using pairwise constraints for person re-identification,” IEEE Trans. Image Process., vol. 28, no. 2, pp. 589–600, Feb. 2019.

Digital Library

[9]

J. Dai, P. Zhang, D. Wang, H. Lu, and H. Wang, “Video person re-identification by temporal residual learning,” IEEE Trans. Image Process., vol. 28, no. 3, pp. 1366–1377, Mar. 2019.

Digital Library

[10]

Z. Feng, J. Lai, and X. Xie, “Learning modality-specific representations for visible-infrared person re-identification,” IEEE Trans. Image Process., vol. 29, pp. 579–590, 2020.

Digital Library

[11]

Z. Zhang, Y. Xie, W. Zhang, Y. Tang, and Q. Tian, “Tensor multi-task learning for person re-identification,” IEEE Trans. Image Process., vol. 29, pp. 2463–2477, 2020.

Digital Library

[12]

D. Chen, S. Zhang, W. Ouyang, J. Yang, and Y. Tai, “Person search by separated modeling and a mask-guided two-stream CNN model,” IEEE Trans. Image Process., vol. 29, pp. 4669–4682, 2020.

Digital Library

[13]

Y. Suh, J. Wang, S. Tang, T. Mei, and K. M. Lee, “Part-aligned bilinear representations for person re-identification,” in Proc. Eur. Conf. Comput. Vis., 2018, pp. 402–419.

[14]

Z. Zhang, C. Lan, W. Zeng, and Z. Chen, “Densely semantically aligned person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 667–676.

[15]

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.

[16]

W. Li, X. Zhu, and S. Gong, “Harmonious attention network for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 2285–2294.

[17]

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, Jun. 2019.

[18]

F. Zhu, X. Kong, L. Zheng, H. Fu, and Q. Tian, “Part-based deep hashing for large-scale person re-identification,” IEEE Trans. Image Process., vol. 26, no. 10, pp. 4806–4817, Oct. 2017.

Digital Library

[19]

F. Zhenget al., “Pyramidal person re-identification via multi-loss dynamic training,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 8514–8522.

[20]

H. Zhaoet al., “Spindle net: Person re-identification with human body region guided feature decomposition and fusion,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 1077–1085.

[21]

Q. Zhouet al., “Robust and efficient graph correspondence transfer for person re-identification,” IEEE Trans. Image Process., vol. 30, pp. 1623–1638, 2019.

Digital Library

[22]

M. S. Sarfraz, A. Schumann, A. Eberle, and R. Stiefelhagen, “A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 420–429.

[23]

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. (ICCV), Oct. 2017, pp. 3960–3969.

[24]

D. Li, X. Chen, Z. Zhang, and K. Huang, “Learning deep context-aware features over body and latent parts for person re-identification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 384–393.

[25]

P. Ren and J. Li, “Factorized distillation: Training holistic person re-identification model by distilling an ensemble of partial ReID models,” 2018, arXiv:1811.08073. [Online]. Available: http://arxiv.org/abs/1811.08073

[26]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 770–778.

[27]

S. Li, S. Bak, P. Carr, and X. Wang, “Diversity regularized spatiotemporal attention for video-based person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 369–378.

[28]

K. Wang, C. Ding, S. J. Maybank, and D. Tao, “CDPM: Convolutional deformable part models for semantically aligned person re-identification,” IEEE Trans. Image Process., vol. 29, pp. 3416–3428, 2020.

Digital Library

[29]

L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, “Scalable person re-identification: A benchmark,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2015, pp. 1116–1124.

[30]

Z. Zheng, L. Zheng, and Y. Yang, “Unlabeled samples generated by GAN improve the person re-identification baseline in vitro,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 3754–3762.

[31]

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., Jun. 2014, pp. 152–159.

[32]

L. Wei, S. Zhang, W. Gao, and Q. Tian, “Person transfer GAN to bridge domain gap for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 79–88.

[33]

T. Chenet al., “ABD-Net: Attentive but diverse person re-identification,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 8350–8360.

[34]

W. Yang, H. Huang, Z. Zhang, X. Chen, K. Huang, and S. Zhang, “Towards rich feature discovery with class activation maps augmentation for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 1389–1398.

[35]

L. Songet al., “Unsupervised domain adaptive re-identification: Theory and practice,” Pattern Recognit., vol. 102, Jun. 2020, Art. no.

Digital Library

[36]

F. Yanget al., “Part-aware progressive unsupervised domain adaptation for person re-identification,” IEEE Trans. Multimedia, early access, Jun. 12, 2020. 10.1109/TMM.2020.3001522.

[37]

Z. Zhong, L. Zheng, Z. Luo, S. Li, and Y. Yang, “Invariance matters: Exemplar memory for domain adaptive person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 598–607.

[38]

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., early access, Sep. 18, 2020. 10.1109/TPAMI.2020.3024900.

[39]

Y. Fuet al., “Horizontal pyramid matching for person re-identification,” in Proc. AAAI Conf. Artif. Intell., 2019, pp. 8295–8302.

[40]

G. Wang, Y. Yuan, X. Chen, J. Li, and X. Zhou, “Learning discriminative features with multiple granularities for person re-identification,” in Proc. 26th ACM Int. Conf. Multimedia, Oct. 2018, pp. 274–282.

[41]

J. Xu, R. Zhao, F. Zhu, H. Wang, and W. Ouyang, “Attention-aware compositional network for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 2119–2128.

[42]

M. M. Kalayeh, E. Basaran, M. Gokmen, M. E. Kamasak, and M. Shah, “Human semantic parsing for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 1062–1071.

[43]

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. (ICCV), Oct. 2017, pp. 3219–3228.

[44]

X. Liuet al., “HydraPlus-Net: Attentive deep features for pedestrian analysis,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 350–359.

[45]

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. Int. Conf. Mach. Learn., 2015, pp. 448–456.

[46]

S. Singh and A. Shrivastava, “EvalNorm: Estimating batch normalization statistics for evaluation,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 3632–3640.

[47]

S. Ioffe, “Batch renormalization: Towards reducing minibatch dependence in batch-normalized models,” in Proc. Adv. Neural Inf. Process. Syst., 2017, pp. 1945–1953.

[48]

W.-G. Chang, T. You, S. Seo, S. Kwak, and B. Han, “Domain-specific batch normalization for unsupervised domain adaptation,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 7346–7354.

[49]

Y. Li, N. Wang, J. Shi, J. Liu, and X. Hou, “Revisiting batch normalization for practical domain adaptation,” in Proc. ICLR, 2017, pp. 1–12.

[50]

H. Zheng, J. Fu, T. Mei, and J. Luo, “Learning multi-attention convolutional neural network for fine-grained image recognition,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 5209–5217.

[51]

M. Simon and E. Rodner, “Neural activation constellations: Unsupervised part model discovery with convolutional networks,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2015, pp. 1143–1151.

[52]

X. Zhang, H. Xiong, W. Zhou, W. Lin, and Q. Tian, “Picking deep filter responses for fine-grained image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 1134–1142.

[53]

N. Wang, S. Ma, J. Li, Y. Zhang, and L. Zhang, “Multistage attention network for image inpainting,” Pattern Recognit., vol. 106, Oct. 2020, Art. no.

[54]

V. Nair and G. Hinton, “Rectified linear units improve restricted Boltzmann machines,” in Proc. 27th Int. Conf. Mach. Learn. (ICML), 2010, pp. 807–814.

[55]

F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2015, pp. 815–823.

[56]

A. Hermans, L. Beyer, and B. Leibe, “In defense of the triplet loss for person re-identification,” 2017, arXiv:1703.07737. [Online]. Available: http://arxiv.org/abs/1703.07737

[57]

J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 7132–7141.

[58]

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627–1645, Sep. 2010.

Digital Library

[59]

Z. Zhong, L. Zheng, D. Cao, and S. Li, “Re-ranking person re-identification with k-reciprocal encoding,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 1318–1327.

[60]

Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, “Random erasing data augmentation,” in Proc. AAAI Conf. Artif. Intell., 2020, pp. 13001–13008.

[61]

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton, “On the importance of initialization and momentum in deep learning,” in Proc. Int. Conf. Mach. Learn., 2013, pp. 1139–1147.

[62]

L. Zheng, H. Zhang, S. Sun, M. Chandraker, Y. Yang, and Q. Tian, “Person re-identification in the wild,” in Proc. IEEE Int. Conf. Comput. Vis., Jul. 2017, pp. 1367–1376.

[63]

L. V. D. Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res., vol. 9, no. 1, pp. 2579–2605, 2008.

[64]

R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 618–626.

[65]

X. Chang, T. M. Hospedales, and T. Xiang, “Multi-level factorisation net for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 2109–2118.

[66]

J. Siet al., “Dual attention matching network for context-aware feature sequence based person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 5363–5372.

[67]

C. Luo, Y. Chen, N. Wang, and Z.-X. Zhang, “Spectral feature transformation for person re-identification,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 4975–4984.

[68]

R. Hou, B. Ma, H. Chang, X. Gu, S. Shan, and X. Chen, “Interaction-and-aggregation network for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 9317–9326.

[69]

X. Chenet al., “Salience-guided cascaded suppression network for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2020, pp. 3300–3310.

[70]

G. Chen, C. Lin, L. Ren, J. Lu, and J. Zhou, “Self-critical attention learning for person re-identification,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 9636–9645.

[71]

X. Jin, C. Lan, W. Zeng, G. Wei, and Z. Chen, “Semantics-aligned representation learning for person re-identification,” in Proc. AAAI Conf. Artif. Intell., 2020, pp. 11173–11180.

[72]

P. Fang, J. Zhou, S. Roy, L. Petersson, and M. Harandi, “Bilinear attention networks for person retrieval,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 8029–8038.

[73]

K. Zhu, H. Guo, Z. Liu, M. Tang, and J. Wang, “Identity-guided human semantic parsing for person re-identification,” in Proc. Eur. Conf. Comput. Vis., 2020, pp. 1–19.

[74]

H. Lingxiao, Y. Wang, W. Liu, H. Zhao, Z. Sun, and J. Feng, “Foreground-aware pyramid reconstruction for alignment-free occluded person re-identification,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2019, pp. 8449–8458.

[75]

Y. Wanget al., “Resource aware person re-identification across multiple resolutions,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, pp. 8042–8051.

[76]

Y. Sun, L. Zheng, W. Deng, and S. Wang, “SVDNet for pedestrian retrieval,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 3800–3808.

[77]

C. Wang, Q. Zhang, C. Huang, W. Liu, and X. Wang, “Mancs: A multi-task attentional network with curriculum sampling for person re-identification,” in Proc. Eur. Conf. Comput. Vis., 2018, pp. 365–381.

[78]

Z. Zheng, X. Yang, Z. Yu, L. Zheng, Y. Yang, and J. Kautz, “Joint discriminative and generative learning for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 2138–2147.

[79]

X. Qian, Y. Fu, T. Xiang, Y.-G. Jiang, and X. Xue, “Leader-based multi-scale attention deep architecture for person re-identification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 371–385, Feb. 2020.

[80]

J. Li, S. Zhang, Q. Tian, M. Wang, and W. Gao, “Pose-guided representation learning for person re-identification,” IEEE Trans. Pattern Anal. Mach. Intell., early access, Jul. 16, 2019. 10.1109/TPAMI.2019.2929036.

[81]

Z. Zhang, C. Lan, W. Zeng, X. Jin, and Z. Chen, “Relation-aware global attention for person re-identification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2020, pp. 3186–3195.

[82]

L. Wei, S. Zhang, H. Yao, W. Gao, and Q. Tian, “GLAD: Global-local-alignment descriptor for pedestrian retrieval,” in Proc. 25th ACM Int. Conf. Multimedia, Oct. 2017, pp. 420–428.

Cited By

Wu XMa WGuo DZhou TZhao SCai ZWooldridge MDy JNatarajan S(2024)Text-based occluded person re-identification via multi-granularity contrastive consistency learningProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i6.28433(6162-6170)Online publication date: 20-Feb-2024
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Wang YYang MCao RCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Fine-grained Semantic Alignment with Transferred Person-SAM for Text-based Person RetrievalProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681553(5432-5441)Online publication date: 28-Oct-2024
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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 30, Issue

2021

5053 pages

ISSN:1057-7149

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Wu XMa WGuo DZhou TZhao SCai ZWooldridge MDy JNatarajan S(2024)Text-based occluded person re-identification via multi-granularity contrastive consistency learningProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i6.28433(6162-6170)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i6.28433
Wang YYang MCao RCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Fine-grained Semantic Alignment with Transferred Person-SAM for Text-based Person RetrievalProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681553(5432-5441)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681553
Yang XWang XLiu LWang NGao X(2024)STFE: A Comprehensive Video-Based Person Re-Identification Network Based on Spatio-Temporal Feature EnhancementIEEE Transactions on Multimedia10.1109/TMM.2024.336213626(7237-7249)Online publication date: 5-Feb-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3362136
Ji HWang LZhou STang WHua G(2024)Disentangled Sample Guidance Learning for Unsupervised Person Re-IdentificationIEEE Transactions on Image Processing10.1109/TIP.2024.345600833(5144-5158)Online publication date: 1-Jan-2024
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Gong TDu GWang JDing YZhang LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Prototype-guided Cross-modal Completion and Alignment for Incomplete Text-based Person Re-identificationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613802(5253-5261)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613802
Wang KDing CPang JXu X(2023)Context Sensing Attention Network for Video-based Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357320319:4(1-20)Online publication date: 27-Feb-2023
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Wang PDing CShao ZHong ZZhang STao D(2023)Quality-Aware Part Models for Occluded Person Re-IdentificationIEEE Transactions on Multimedia10.1109/TMM.2022.315628225(3154-3165)Online publication date: 1-Jan-2023
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Wang PDing CTan WGong MJia KTao D(2023)Uncertainty-Aware Clustering for Unsupervised Domain Adaptive Object Re-IdentificationIEEE Transactions on Multimedia10.1109/TMM.2022.314962925(2624-2635)Online publication date: 1-Jan-2023
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Xie GWen X(2023)RMHNet: A Relation-Aware Multi-granularity Hierarchical Network for Person Re-identificationNeural Processing Letters10.1007/s11063-022-10946-y55:2(1433-1454)Online publication date: 1-Apr-2023
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Jiao BGao LWang POria VSapino MSatoh SKerhervé BCheng WIde ISingh V(2022)Temporal-Consistent Visual Clue Attentive Network for Video-Based Person Re-IdentificationProceedings of the 2022 International Conference on Multimedia Retrieval10.1145/3512527.3531362(72-80)Online publication date: 27-Jun-2022
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