Lifelong Visible-Infrared Person Re-Identification via a Tri-Token Transformer with a Query-Key Mechanism
Pages 988 - 997
Abstract
Visible-infrared person re-identification has been extensively explored, but it typically relies on stationary datasets for training. However, data is collected in a streaming manner in practical scenarios, necessitating the model's ability to continually learn without forgetting previous tasks. Existing methods focus simply on the lifelong single-modality person re-identification, but the visible images are sometimes unavailable, e.g., at night. To this end, this paper introduces a more challenging yet practical problem: Lifelong Visible-Infrared Person Re-identification (LVI-ReID). Inspired by the complementary learning systems, we propose a Tri-Token transformer with a Query-Key mechanism (TTQK) to tackle the LVI-ReID. Firstly, a general token is designed to capture robust domain-general features, shared across different domains, aiming to enhance the generalization capability. Subsequently, recognizing that different domains possess unique features like illumination and scenes, we allocate a specific token for each domain to extract significant domain-specific features, aiming to enhance the adaptability across domains. Furthermore, to prevent using the task identifier in the inference stage of LVI-ReID, we design a query-key mechanism to adaptively select the appropriate specific token based on the similarity between the query token and keys. Extensive experiments demonstrate that our method outperforms other lifelong learning and LReID methods. The source code of our designed LVI-ReID method is at https://github.com/SWU-CS-MediaLab/TTQK.
References
[1]
Xiaobin Chang, Timothy M. Hospedales, and Tao Xiang. 2018. Multi-Level Factorisation Net for Person Re-identification. In 2018 IEEE /CVF Conference on Computer Vision and Pattern Recognition. IEEE, Salt Lake City, UT, 2109--2118. https://doi.org/10.1109/CVPR.2018.00225
[2]
Cuiqun Chen, Mang Ye, Meibin Qi, Jingjing Wu, Jianguo Jiang, and Chia-Wen Lin. 2022. Structure-Aware Positional Transformer for Visible-Infrared Person Re-Identification. IEEE Transactions on Image Processing, Vol. 31 (2022), 2352--2364. https://doi.org/10.1109/TIP.2022.3141868
[3]
Seokeon Choi, Sumin Lee, Youngeun Kim, Taekyung Kim, and Changick Kim. 2020. Hi-CMD : Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification. In 2020 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). IEEE, Seattle, WA, USA, 10254--10263. https://doi.org/10.1109/CVPR42600.2020.01027
[4]
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle. 2020. PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning. In Computer Vision textendash ECCV 2020, Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Vol. 12365. Springer International Publishing, Cham, 86--102. https://doi.org/10.1007/978-3-030-58565-5_6
[5]
Arthur Douillard, Alexandre Rame, Guillaume Couairon, and Matthieu Cord. 2022. DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion. In 2022 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). IEEE, New Orleans, LA, USA, 9275--9285. https://doi.org/10.1109/CVPR52688.2022.00907
[6]
Jiawei Feng, Ancong Wu, and Wei-Shi Zheng. 2023. Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification. In 2023 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). 22752--22761. https://doi.org/10.1109/CVPR52729.2023.02179
[7]
Zhanxiang Feng, Jianhuang Lai, and Xiaohua Xie. 2020. Learning Modality-Specific Representations for Visible-Infrared Person Re-Identification. IEEE Transactions on Image Processing, Vol. 29 (2020), 579--590. https://doi.org/10.1109/TIP.2019.2928126
[8]
Wenhang Ge, Junlong Du, Ancong Wu, Yuqiao Xian, Ke Yan, Feiyue Huang, and Wei-Shi Zheng. 2022. Lifelong Person Re-identification by Pseudo Task Knowledge Preservation. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36, 1 (June 2022), 688--696. https://doi.org/10.1609/aaai.v36i1.19949
[9]
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. 2019. Learning a Unified Classifier Incrementally via Rebalancing. In 2019 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). IEEE, Long Beach, CA, USA, 831--839. https://doi.org/10.1109/CVPR.2019.00092
[10]
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen. 2019. Compacting, Picking and Growing for Unforgetting Continual Learning. In Advances in Neural Information Processing Systems, Vol. 32. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2019/hash/3b220b436e5f3d917a1e649a0dc0281c-Abstract.html
[11]
Ferenc Huszár. 2018. On Quadratic Penalties in Elastic Weight Consolidation. Proceedings of the National Academy of Sciences, Vol. 115, 11 (March 2018). https://doi.org/10.1073/pnas.1717042115
[12]
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. 2017. Overcoming Catastrophic Forgetting in Neural Networks. Proceedings of the National Academy of Sciences, Vol. 114, 13 (March 2017), 3521--3526. https://doi.org/10.1073/pnas.1611835114
[13]
Dharshan Kumaran, Demis Hassabis, and James L. McClelland. 2016. What Learning Systems Do Intelligent Agents Need ? Complementary Learning Systems Theory Updated. Trends in Cognitive Sciences, Vol. 20, 7 (July 2016), 512--534. https://doi.org/10.1016/j.tics.2016.05.004
[14]
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang. 2017. Overcoming Catastrophic Forgetting by Incremental Moment Matching. In Advances in Neural Information Processing Systems, Vol. 30. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/hash/f708f064faaf32a43e4d3c784e6af9ea-Abstract.html
[15]
Xiaorong Li, Shipeng Wang, Jian Sun, and Zongben Xu. 2023. Variational Data-Free Knowledge Distillation for Continual Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 10 (Oct. 2023), 12618--12634. https://doi.org/10.1109/TPAMI.2023.3271626
[16]
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong. 2019. Learn to Grow : A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting. In Proceedings of the 36th International Conference on Machine Learning. PMLR, 3925--3934. https://proceedings.mlr.press/v97/li19m.html
[17]
Zhizhong Li and Derek Hoiem. 2018. Learning without Forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, 12 (Dec. 2018), 2935--2947. https://doi.org/10.1109/TPAMI.2017.2773081
[18]
Tengfei Liang, Yi Jin, Yajun Gao, Wu Liu, Songhe Feng, Tao Wang, and Yidong Li. 2023. CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification. IEEE Transactions on Multimedia (2023), 1--13. https://doi.org/10.1109/TMM.2023.3237155
[19]
Xinyu Lin, Jinxing Li, Zeyu Ma, Huafeng Li, Shuang Li, Kaixiong Xu, Guangming Lu, and David Zhang. 2022. Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification. In 2022 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). 20941--20950. https://doi.org/10.1109/CVPR52688.2022.02030
[20]
Fangyi Liu and Lei Zhang. 2019. View Confusion Feature Learning for Person Re-Identification. In 2019 IEEE /CVF International Conference on Computer Vision (ICCV ). IEEE, Seoul, Korea (South), 6638--6647. https://doi.org/10.1109/ICCV.2019.00674
[21]
Haijun Liu, Jian Cheng, Wen Wang, Yanzhou Su, and Haiwei Bai. 2020. Enhancing the Discriminative Feature Learning for Visible-Thermal Cross-Modality Person Re-Identification. Neurocomputing, Vol. 398 (July 2020), 11--19. https://doi.org/10.1016/j.neucom.2020.01.089
[22]
Hu Lu, Xuezhang Zou, and Pingping Zhang. 2023. Learning Progressive Modality-Shared Transformers for Effective Visible-Infrared Person Re-identification. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2 (June 2023), 1835--1843. https://doi.org/10.1609/aaai.v37i2.25273
[23]
James L. McClelland, Bruce L. McNaughton, and Randall C. O'Reilly. 1995. Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights from the Successes and Failures of Connectionist Models of Learning and Memory. Psychological Review, Vol. 102, 3 (1995), 419--457. https://doi.org/10.1037/0033-295X.102.3.419
[24]
Dat Tien Nguyen, Hyung Gil Hong, Ki Wan Kim, and Kang Ryoung Park. 2017. Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras. Sensors, Vol. 17, 3 (March 2017), 605. https://doi.org/10.3390/s17030605
[25]
Nan Pu, Wei Chen, Yu Liu, Erwin M. Bakker, and Michael S. Lew. 2021. Lifelong Person Re-Identification via Adaptive Knowledge Accumulation. In 2021 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). 7897--7906. https://doi.org/10.1109/CVPR46437.2021.00781
[26]
Jathushan Rajasegaran, Munawar Hayat, Salman H Khan, Fahad Shahbaz Khan, and Ling Shao. 2019. Random Path Selection for Continual Learning. In Advances in Neural Information Processing Systems, Vol. 32. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2019/hash/83da7c539e1ab4e759623c38d8737e9e-Abstract.html
[27]
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert. 2017. iCaRL: Incremental Classifier and Representation Learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ). 5533--5542. https://doi.org/10.1109/CVPR.2017.587
[28]
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou. 2018. Overcoming Catastrophic Forgetting with Hard Attention to the Task. In Proceedings of the 35th International Conference on Machine Learning. PMLR, 4548--4557. https://proceedings.mlr.press/v80/serra18a.html
[29]
Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. 2018. Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline ). In Computer Vision textendash ECCV 2018, Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss (Eds.). Vol. 11208. Springer International Publishing, Cham, 501--518. https://doi.org/10.1007/978-3-030-01225-0_30
[30]
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Herve Jegou. 2021. Going Deeper with Image Transformers. In 2021 IEEE /CVF International Conference on Computer Vision (ICCV ). IEEE, Montreal, QC, Canada, 32--42. https://doi.org/10.1109/ICCV48922.2021.00010
[31]
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data Using T-SNE. Journal of Machine Learning Research, Vol. 9, 86 (2008), 2579--2605. http://jmlr.org/papers/v9/vandermaaten08a.html
[32]
Guan-An Wang, Tianzhu Zhang, Jian Cheng, Si Liu, Yang Yang, and Zengguang Hou. 2019b. RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment. In 2019 IEEE /CVF International Conference on Computer Vision (ICCV ). 3622--3631. https://doi.org/10.1109/ICCV.2019.00372
[33]
Guan-An Wang, Tianzhu Zhang, Yang Yang, Jian Cheng, Jianlong Chang, Xu Liang, and Zeng-Guang Hou. 2020. Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 07 (April 2020), 12144--12151. https://doi.org/10.1609/aaai.v34i07.6894
[34]
Zhixiang Wang, Zheng Wang, Yinqiang Zheng, Yung-Yu Chuang, and Shin'ich Satoh. 2019a. Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-Identification. In 2019 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). IEEE, Long Beach, CA, USA, 618--626. https://doi.org/10.1109/CVPR.2019.00071
[35]
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. 2022. Learning to Prompt for Continual Learning. In 2022 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). IEEE, New Orleans, LA, USA, 139--149. https://doi.org/10.1109/CVPR52688.2022.00024
[36]
Ancong Wu, Wei-Shi Zheng, Hong-Xing Yu, Shaogang Gong, and Jianhuang Lai. 2017. RGB-Infrared Cross-Modality Person Re-identification. In 2017 IEEE International Conference on Computer Vision (ICCV ). 5390--5399. https://doi.org/10.1109/ICCV.2017.575
[37]
Guile Wu and Shaogang Gong. 2021. Generalising without Forgetting for Lifelong Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 4 (May 2021), 2889--2897. https://doi.org/10.1609/aaai.v35i4.16395
[38]
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu. 2019. Large Scale Incremental Learning. In 2019 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). 374--382. https://doi.org/10.1109/CVPR.2019.00046
[39]
Shipeng Yan, Jiangwei Xie, and Xuming He. 2021. DER: Dynamically Expandable Representation for Class Incremental Learning. In 2021 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). IEEE, Nashville, TN, USA, 3013--3022. https://doi.org/10.1109/CVPR46437.2021.00303
[40]
Mang Ye, Xiangyuan Lan, Jiawei Li, and Pong Yuen. 2018. Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32. https://doi.org/10.1609/aaai.v32i1.12293
[41]
Mang Ye, Xiangyuan Lan, Zheng Wang, and Pong C. Yuen. 2020. Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification. IEEE Transactions on Information Forensics and Security, Vol. 15 (2020), 407--419. https://doi.org/10.1109/TIFS.2019.2921454
[42]
Mang Ye, Weijian Ruan, Bo Du, and Mike Zheng Shou. 2021a. Channel Augmented Joint Learning for Visible-Infrared Recognition. In 2021 IEEE /CVF International Conference on Computer Vision (ICCV ). IEEE, Montreal, QC, Canada, 13547--13556. https://doi.org/10.1109/ICCV48922.2021.01331
[43]
Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven C. H. Hoi. 2022. Deep Learning for Person Re-Identification: A Survey and Outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 6 (June 2022), 2872--2893. https://doi.org/10.1109/TPAMI.2021.3054775
[44]
Mang Ye, Jianbing Shen, and Ling Shao. 2021b. Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learning. IEEE Transactions on Information Forensics and Security, Vol. 16 (2021), 728--739. https://doi.org/10.1109/TIFS.2020.3001665
[45]
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang. 2018. Lifelong Learning with Dynamically Expandable Networks. arxiv: 1708.01547 http://arxiv.org/abs/1708.01547
[46]
Chunlin Yu, Ye Shi, Zimo Liu, Shenghua Gao, and Jingya Wang. 2023. Lifelong Person Re-identification via Knowledge Refreshing and Consolidation. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 3 (June 2023), 3295--3303. https://doi.org/10.1609/aaai.v37i3.25436
[47]
Friedemann Zenke, Ben Poole, and Surya Ganguli. 2017. Continual Learning Through Synaptic Intelligence. In Proceedings of the 34th International Conference on Machine Learning. PMLR, 3987--3995. https://proceedings.mlr.press/v70/zenke17a.html
[48]
Haoyu Zhang, Meng Liu, Yuhong Li, Ming Yan, Zan Gao, Xiaojun Chang, and Liqiang Nie. 2023. Attribute-Guided Collaborative Learning for Partial Person Re-Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 12 (Dec. 2023), 14144--14160. https://doi.org/10.1109/TPAMI.2023.3312302
[49]
Yukang Zhang and Hanzi Wang. 2023. Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification. In 2023 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). 2153--2162. https://doi.org/10.1109/CVPR52729.2023.00214
[50]
Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, and Shu-Tao Xia. 2020. Maintaining Discrimination and Fairness in Class Incremental Learning. In 2020 IEEE /CVF Conference on Computer Vision and Pattern Recognition (CVPR ). IEEE, Seattle, WA, USA, 13205--13214. https://doi.org/10.1109/CVPR42600.2020.01322
[51]
Liming Zhao, Xi Li, Yueting Zhuang, and Jingdong Wang. 2017. Deeply-Learned Part-Aligned Representations for Person Re-identification. In 2017 IEEE International Conference on Computer Vision (ICCV ). IEEE, Venice, 3239--3248. https://doi.org/10.1109/ICCV.2017.349
[52]
Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, and Qi Tian. 2017. Person Re-identification in the Wild. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ). IEEE, Honolulu, HI, 3346--3355. https://doi.org/10.1109/CVPR.2017.357
[53]
Jianqing Zhu, Liu Liu, Yibing Zhan, Xiaobin Zhu, Huanqiang Zeng, and Dacheng Tao. 2023. Attribute-Image Person Re-identification via Modal-Consistent Metric Learning. International Journal of Computer Vision, Vol. 131, 11 (Nov. 2023), 2959--2976. https://doi.org/10.1007/s11263-023-01841-7
[54]
Yuanxin Zhu, Zhao Yang, Li Wang, Sai Zhao, Xiao Hu, and Dapeng Tao. 2020. Hetero-Center Loss for Cross-Modality Person Re-identification. Neurocomputing, Vol. 386 (April 2020), 97--109. https://doi.org/10.1016/j.neucom.2019.12.100
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May 2024
1379 pages
ISBN:9798400706196
DOI:10.1145/3652583
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- Shin'ichi Satoh,
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