{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:46:50Z","timestamp":1770288410005,"version":"3.49.0"},"reference-count":62,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and technology projects of State Grid Corporation","award":["1400-202157214A-0-0-00"],"award-info":[{"award-number":["1400-202157214A-0-0-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Person re-identification is essential to intelligent video analytics, whose results affect downstream tasks such as behavior and event analysis. However, most existing models only consider the accuracy, rather than the computational complexity, which is also an aspect to consider in practical deployment. We note that self-attention is a powerful technique for representation learning. It can work with convolution to learn more discriminative feature representations for re-identification. We propose an improved multi-scale feature learning structure, DM-OSNet, with better performance than the original OSNet. Our DM-OSNet replaces the 9\u00d79 convolutional stream in OSNet with multi-head self-attention. To maintain model efficiency, we use double-layer multi-head self-attention to reduce the computational complexity of the original multi-head self-attention. The computational complexity is reduced from the original O((H\u00d7W)2) to O(H\u00d7W\u00d7G2). To further improve the model performance, we use SpCL to perform unsupervised pre-training on the large-scale unlabeled pedestrian dataset LUPerson. Finally, our DM-OSNet achieves an mAP of 87.36%, 78.26%, 72.96%, and 57.13% on the Market1501, DukeMTMC-reID, CUHK03, and MSMT17 datasets.<\/jats:p>","DOI":"10.3390\/s22166293","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"6293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Integration of Multi-Head Self-Attention and Convolution for Person Re-Identification"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4784-6794","authenticated-orcid":false,"given":"Yalei","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"},{"name":"School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China"},{"name":"Yangzhong Intelligent Electric Research Center, North China Electric Power University, Yangzhong 212211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunguang","family":"Liu","sequence":"additional","affiliation":[{"name":"Yangzhong Intelligent Electric Research Center, North China Electric Power University, Yangzhong 212211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenli","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,21]]},"reference":[{"key":"ref_1","unstructured":"Zheng, L., Yang, Y., and Hauptmann, A.G. (2016). Person Re-identification: Past, Present and Future. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, L., Li, X., Wang, J., and Zhuang, Y. (2017). Deeply-Learned Part-Aligned Representations for Person Re-Identification. arXiv.","DOI":"10.1109\/ICCV.2017.349"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Suh, Y., Wang, J., Tang, S., Mei, T., and Lee, K.M. (2018). Part-Aligned Bilinear Representations for Person Re-identification. arXiv.","DOI":"10.1007\/978-3-030-01264-9_25"},{"key":"ref_4","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. arXiv.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_6","unstructured":"Zhou, K., Yang, Y., Cavallaro, A., and Xiang, T. (November, January 27). Omni-Scale Feature Learning for Person Re-Identification. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fu, D., Chen, D., Bao, J., Yang, H., Yuan, L., Zhang, L., Li, H., and Chen, D. (2021, January 20\u201325). Unsupervised pre-training for person re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01451"},{"key":"ref_8","unstructured":"Luo, H., Wang, P., Xu, Y., Ding, F., Zhou, Y., Wang, F., Li, H., and Jin, R. (2022). Self-Supervised Pre-Training for Transformer-Based Person Re-Identification. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jin, X., He, T., Yin, Z., Shen, X., Liu, T., Wang, X., Huang, J., Hua, X.S., and Chen, Z. (2022). Meta Clustering Learning for Large-scale Unsupervised Person Re-identification. arXiv.","DOI":"10.1145\/3503161.3547900"},{"key":"ref_10","first-page":"11309","article-title":"Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID","volume":"33","author":"Ge","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_12","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., and Tian, Q. (2017, January 26). Person Re-identification in the Wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.357"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cheng, D., Gong, Y., Zhou, S., Wang, J., and Zheng, N. (2016, January 27\u201330). Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.149"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, X., Zhang, Z., and Huang, K. (2017, January 26). Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.782"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, H., Maoqing, T., Sun, S., Shao, J., Yan, J., Yi, S., Wang, X., and Tang, X. (2017, January 26). Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.103"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lan, C., Zeng, W., and Chen, Z. (2019, January 15\u201320). Densely Semantically Aligned Person Re-Identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00076"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, J., Yuan, Y., Huang, L., Zhang, C., Yao, J.G., and Han, K. (2019, January 15\u201320). Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00374"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., and Wang, S. (2018, January 8\u201314). Beyond Part Models: Person Retrieval with Refined Part Pooling. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, G., Yuan, Y., Chen, X., Li, J., and Zhou, X. (2018, January 11\u201314). Learning Discriminative Features with Multiple Granularities for Person Re-Identification. Proceedings of the 26th ACM International Conference on Multimedia, Yokohama, Japan.","DOI":"10.1145\/3240508.3240552"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Vanhoucke, V., Rabinovich, A., and Erhan, D. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 26). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chang, X., Hospedales, T.M., and Xiang, T. (2018, January 18\u201322). Multi-level factorisation net for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00225"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qian, X., Fu, Y., Jiang, Y.G., Xiang, T., and Xue, X. (2017, January 22\u201329). Multi-scale deep learning architectures for person re-identification. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.577"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhu, X., and Gong, S. (2017, January 22\u201329). Person re-identification by deep learning multi-scale representations. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.304"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107281","DOI":"10.1016\/j.knosys.2021.107281","article-title":"Person re-identification based on multi-scale feature learning","volume":"228","author":"Li","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, Z., Qin, W., Luo, F., Guan, T., Xie, F., Han, S., and Sun, D. (2021). Combination of validity aggregation and multi-scale feature for person re-identification. J. Ambient. Intell. Humaniz. Comput., 1\u201316.","DOI":"10.1007\/s12652-021-03473-6"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TETCI.2020.3034606","article-title":"Attention deep model with multi-scale deep supervision for person re-identification","volume":"5","author":"Wu","year":"2021","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huang, W., Li, Y., Zhang, K., Hou, X., Xu, J., Su, R., and Xu, H. (2021). An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification. Appl. Sci., 11.","DOI":"10.3390\/app11052010"},{"key":"ref_32","first-page":"e447","article-title":"Stochastic attentions and context learning for person re-identification","volume":"7","author":"Perwaiz","year":"2021","journal-title":"PeerJ"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, W., Zhu, X., and Gong, S. (2018, January 18\u201322). Harmonious Attention Network for Person Re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00243"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.1109\/TIP.2017.2700762","article-title":"End-to-End Comparative Attention Networks for Person Re-identification","volume":"26","author":"Liu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","unstructured":"Chen, T., Ding, S., Xie, J., Yuan, Y., Chen, W., Yang, Y., Ren, Z., and Wang, Z. (November, January 27). ABD-Net: Attentive but Diverse Person Re-Identification. Proceedings of the International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_36","unstructured":"Chen, G., Lin, C., Ren, L., Lu, J., and Zhou, J. (November, January 27). Self-Critical Attention Learning for Person Re-Identification. Proceedings of the International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_37","unstructured":"Bryan, B., Gong, Y., Zhang, Y., and Poellabauer, C. (November, January 27). Second-Order Non-Local Attention Networks for Person Re-Identification. Proceedings of the International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201322). Non-local Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_39","unstructured":"Zhou, S., Wang, F., Huang, Z., and Wang, J. (November, January 27). Discriminative Feature Learning With Consistent Attention Regularization for Person Re-Identification. Proceedings of the International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_40","first-page":"3058","article-title":"Attention is All you Need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Neural Inf. Process. Syst."},{"key":"ref_41","unstructured":"Zhu, K., Guo, H., Zhang, S., Wang, Y., Huang, G., Qiao, H., Liu, J., Wang, J., and Tang, M. (2021). AAformer: Auto-Aligned Transformer for Person Re-Identification. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, S., Luo, H., Wang, P., Wang, F., Li, H., and Jiang, W. (2021). TransReID: Transformer-based Object Re-Identification. arXiv.","DOI":"10.1109\/ICCV48922.2021.01474"},{"key":"ref_43","unstructured":"Bello, I., Zoph, B., Vaswani, A., Shlens, J., and Le, Q.V. (November, January 27). Attention Augmented Convolutional Networks. Proceedings of the Computer Vision and Pattern Recognition, Seoul, Korea."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021, January 20\u201325). Bottleneck Transformers for Visual Recognition. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, G., Zhang, P., Qi, J., and Lu, H. (2021, January 20\u201324). HAT: Hierarchical Aggregation Transformers for Person Re-identification. Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China.","DOI":"10.1145\/3474085.3475202"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., and Wu, F. (2021, January 20\u201325). Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00292"},{"key":"ref_47","unstructured":"Liu, Y., Sun, G., Qiu, Y., Zhang, L., Chhatkuli, A., and Gool, L.V. (2021, January 20\u201325). Transformer in Convolutional Neural Networks. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA."},{"key":"ref_48","unstructured":"Zhang, L., Wu, X., Zhang, S., and Yin, Z. (2020). Branch-Cooperative OSNet for Person Re-Identification. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Herzog, F., Ji, X., Teepe, T., H\u00f6rmann, S., Gilg, J., and Rigoll, G. (2020, January 19\u201322). Lightweight Multi-Branch Network for Person Re-Identification. Proceedings of the 2021 IEEE International Conference on Image Processing, Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506733"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and Tian, Q. (2015, January 7\u201313). Scalable Person Re-identification: A Benchmark. Proceedings of the International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.133"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ristani, E., Solera, F., Zou, R.S., Cucchiara, R., and Tomasi, C. (2016, January 27\u201330). Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, W., Zhao, R., Xiao, T., and Wang, X. (2014, January 23\u201328). DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. Proceedings of the CVPR, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.27"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Gao, W., and Tian, Q. (2017, January 26). Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00016"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Cao, D., and Li, S. (2017, January 26). Re-ranking Person Re-identification with k-Reciprocal Encoding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.389"},{"key":"ref_55","unstructured":"He, L., Liao, X., Liu, W., Liu, X., Cheng, P., and Mei, T. (2020). FastReID: A Pytorch Toolbox for General Instance Re-identification. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Tay, C.P., Roy, S., and Yap, K.H. (2019, January 15\u201320). AANet: Attribute Attention Network for Person Re-Identifications. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00730"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yu, Z., Zheng, L., Yang, Y., Kautz, J., Yang, X., and Zheng, Z. (2019, January 15\u201320). Joint Discriminative and Generative Learning for Person Re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00224"},{"key":"ref_58","unstructured":"Quan, R., Dong, X., Wu, Y., Zhu, L., and Yang, Y. (November, January 27). Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_59","unstructured":"Zhu, S., Gu, X., Dai, Z., Tan, P., and Chen, M. (2018, January 2\u20136). Batch DropBlock Network for Person Re-identification and Beyond. Proceedings of the International Conference on Computer Vision, Perth, Australia."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., and Chen, X. (2019, January 15\u201320). Interaction-And-Aggregation Network for Person Re-Identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00954"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, S., Huang, H., Huang, K., Zhang, Z., Yang, W., and Chen, X. (2019, January 15\u201320). Towards Rich Feature Discovery With Class Activation Maps Augmentation for Person Re-Identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00148"},{"key":"ref_62","unstructured":"Deng, W., Chen, B., and Hu, J. (November, January 27). Mixed High-Order Attention Network for Person Re-Identification. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6293\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:13:15Z","timestamp":1760141595000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":62,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22166293"],"URL":"https:\/\/doi.org\/10.3390\/s22166293","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,21]]}}}