{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:15:39Z","timestamp":1740147339447,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s11760-021-02099-7","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T20:02:59Z","timestamp":1642708979000},"page":"1463-1470","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["FLSRNet: pedestrian attribute recognition using focal label smoothing regularization"],"prefix":"10.1007","volume":"16","author":[{"given":"Yazhi","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Gui Peng David","family":"Yam","sequence":"additional","affiliation":[]},{"given":"Jiahao","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Zhen-Peng","family":"Bian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4084-6911","authenticated-orcid":false,"given":"Jing","family":"Tian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"2099_CR1","unstructured":"Wang, X., Zheng, S., Yang, R., Luo, B., Tang, J.: Pedestrian attribute recognition: a survey, arXiv preprint arXiv:1901.07474, 2019. [Online]. Available: https:\/\/arxiv.org\/abs\/1901.07474"},{"issue":"6","key":"2099_CR2","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1007\/s11760-020-01662-y","volume":"14","author":"M Saeidi","year":"2020","unstructured":"Saeidi, M., Ahmadi, A.: A novel approach for deep pedestrian detection based on changes in camera viewing angle. Signal Image Video Process. 14(6), 1273\u20131281 (2020)","journal-title":"Signal Image Video Process."},{"issue":"4","key":"2099_CR3","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1007\/s11760-018-1406-6","volume":"13","author":"Z Li","year":"2019","unstructured":"Li, Z., Chen, Z., Wu, Q., Liu, C.: Real-time pedestrian detection with deep supervision in the wild. Signal Image Video Process. 13(4), 761\u2013769 (2019)","journal-title":"Signal Image Video Process."},{"issue":"4","key":"2099_CR4","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s11760-019-01523-3","volume":"15","author":"Q Wu","year":"2021","unstructured":"Wu, Q., Dai, P., Chen, P., Huang, Y.: Deep adversarial data augmentation with attribute guided for person re-identification. Signal Image Video Process. 15(4), 655\u2013662 (2021)","journal-title":"Signal Image Video Process."},{"key":"2099_CR5","doi-asserted-by":"crossref","unstructured":"Liu, X., Chen, S., Song, L., Wozniak, M., Liu, S.: Self-attention negative feedback network for real-time image super-resolution. J. King Saud Univ. Comput. Inf. Sci. (2021)","DOI":"10.1016\/j.jksuci.2021.07.014"},{"key":"2099_CR6","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, X., Huang, K.: Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios. In: IAPR Asian Conf. on Pattern Recognition, Kuala Lumpur, Malaysia, pp. 111\u2013115 (2015)","DOI":"10.1109\/ACPR.2015.7486476"},{"key":"2099_CR7","doi-asserted-by":"crossref","unstructured":"Sudowe, P., Spitzer, H., Leibe, B.: Person attribute recognition with a jointly-trained holistic CNN model. In: IEEE Int, pp. 87\u201395. Santiago, Chile, Conf. on Computer Vision (2015)","DOI":"10.1109\/ICCVW.2015.51"},{"key":"2099_CR8","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Yu, K., Leng, B., Zhang, Z., Li, D., Huang, K.: Weakly-supervised learning of mid-level features for pedestrian attribute recognition and localization. In: British Machine Vision Conference, London, United Kingdom, pp. 69.1\u201369.12 (2017)","DOI":"10.5244\/C.31.69"},{"key":"2099_CR9","doi-asserted-by":"crossref","unstructured":"Tang, C., Sheng, L., Zhang, Z.-X., Hu, X.: Improving pedestrian attribute recognition with weakly-supervised multi-scale attribute-specific localization. In: IEEE Int, pp. 4996\u20135005. Seoul, Korea, Conf. on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00510"},{"key":"2099_CR10","doi-asserted-by":"publisher","first-page":"103981","DOI":"10.1016\/j.imavis.2020.103981","volume":"102","author":"E Yaghoubi","year":"2020","unstructured":"Yaghoubi, E., Borza, D., Neves, J., Kumar, A., Proen\u00e7a, H.: An attention-based deep learning model for multiple pedestrian attributes recognition. Image Vis. Comput. 102, 103981 (2020)","journal-title":"Image Vis. Comput."},{"issue":"12","key":"2099_CR11","doi-asserted-by":"publisher","first-page":"6126","DOI":"10.1109\/TIP.2019.2919199","volume":"28","author":"Z Tan","year":"2019","unstructured":"Tan, Z., Yang, Y., Wan, J., Hang, H., Guo, G., Li, S.Z.: Attention-based pedestrian attribute analysis. IEEE Trans. Image Process. 28(12), 6126\u20136140 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"2099_CR12","unstructured":"Sarfraz, M. S., Schumann, A., Wang, Y., Stiefelhagen, R.: Deep view-sensitive pedestrian attribute inference in an end-to-end model. In: British Machine Vision Conference, London, United Kingdom, pp. 134.1\u2013134.13 (2017)"},{"issue":"11","key":"2099_CR13","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1109\/TMM.2017.2696825","volume":"19","author":"X Zhang","year":"2017","unstructured":"Zhang, X., Jia, J., Gao, K., Zhang, Y., Zhang, D., Li, J., Tian, Q.: Trip outfits advisor: location-oriented clothing recommendation. IEEE Trans. Multimed. 19(11), 2533\u20132544 (2017)","journal-title":"IEEE Trans. Multimed."},{"key":"2099_CR14","doi-asserted-by":"crossref","unstructured":"Zeng, H., Ai, H., Zhuang, Z., Chen, L.: Multi-task learning via co-attentive sharing for pedestrian attribute recognition. In: IEEE Int. Conf. on Multimedia and Expo, London, United Kingdom (2020)","DOI":"10.1109\/ICME46284.2020.9102757"},{"key":"2099_CR15","doi-asserted-by":"crossref","unstructured":"Wozniak, M., Silka, J., Wieczorek, M.: Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput. Appl. (2021)","DOI":"10.1007\/s00521-021-05841-x"},{"key":"2099_CR16","doi-asserted-by":"publisher","first-page":"107788","DOI":"10.1016\/j.patcog.2020.107788","volume":"112","author":"W Dong","year":"2021","unstructured":"Dong, W., Wu, J., Bai, Z., Hu, Y., Li, W., Qiao, W., Wozniak, M.: MobileGCN applied to low-dimensional node feature learning. Pattern Recogn. 112, 107788 (2021)","journal-title":"Pattern Recogn."},{"key":"2099_CR17","doi-asserted-by":"crossref","unstructured":"Deng, Y., Luo, P., Loy, C.C., Tang, X.: Pedestrian attribute recognition at far distance. In: ACM Int, pp. 789\u2013792. Orlando, USA, Conf. on Multimedia (2014)","DOI":"10.1145\/2647868.2654966"},{"issue":"4","key":"2099_CR18","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1109\/TIP.2018.2878349","volume":"28","author":"D Li","year":"2019","unstructured":"Li, D., Zhang, Z., Chen, X., Huang, K.: A richly annotated pedestrian dataset for person retrieval in real surveillance scenarios. IEEE Trans. Image Process. 28(4), 1575\u20131590 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"2099_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the Inception architecture for computer vision. In: IEEE Int, pp. 2818\u20132826. Los Alamitos, USA, Conf. on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"2099_CR20","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.neucom.2020.02.094","volume":"401","author":"S Liu","year":"2020","unstructured":"Liu, S., Guo, H., Hu, J.-G., Zhao, X., Zhao, C., Wang, T., Zhu, Y., Wang, J., Tang, M.: A novel data augmentation scheme for pedestrian detection with attribute preserving GAN. Neurocomputing 401, 123\u2013132 (2020)","journal-title":"Neurocomputing"},{"key":"2099_CR21","doi-asserted-by":"crossref","unstructured":"Fukui, H., Yamashita, T., Yamauchi, Y., Fujiyoshi, H., Murase, H.: Robust pedestrian attribute recognition for an unbalanced dataset using mini-batch training with rarity rate. In: IEEE Intelligent Vehicles Symposium, pp. 322\u2013327. Sweden, Gothenburg (2016)","DOI":"10.1109\/IVS.2016.7535405"},{"key":"2099_CR22","doi-asserted-by":"crossref","unstructured":"Sarafianos, N., Xu, X., Kakadiaris, I.A.: Deep imbalanced attribute classification using visual attention aggregation. In: Conf, European (ed.) on Computer Vision, pp. 680\u2013697. Munich, Germany (2018)","DOI":"10.1007\/978-3-030-01252-6_42"},{"key":"2099_CR23","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.patrec.2019.01.010","volume":"120","author":"Z Ji","year":"2019","unstructured":"Ji, Z., He, E., Wang, H., Yang, A.: Image-attribute reciprocally guided attention network for pedestrian attribute recognition. Pattern Recogn. Lett. 120, 89\u201395 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"2099_CR24","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.patrec.2020.07.018","volume":"138","author":"Z Ji","year":"2020","unstructured":"Ji, Z., Hu, Z., He, E., Han, J., Pang, Y.: Pedestrian attribute recognition based on multiple time steps attention. Pattern Recog. Lett. 138, 170\u2013176 (2020)","journal-title":"Pattern Recog. Lett."},{"key":"2099_CR25","unstructured":"Pereyra, G., Tucker, G., Chorowski, J., Kaiser, L., Hinton, G.: Regularizing neural networks by penalizing confident output distributions. In: Int. Conf. on Learning Representations, Toulon, France (2017)"},{"key":"2099_CR26","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.E.: When does label smoothing help?\u2019. In: Advances in Neural Information Processing Systems, pp. 4694\u20134703. Canada, Vancouver (2019)"},{"key":"2099_CR27","doi-asserted-by":"crossref","unstructured":"Yuan, L., Tay, F.E., Li, G., Wang, T., Feng, J.: Revisiting knowledge distillation via label smoothing regularization. In: IEEE Int, pp. 3902\u20133910. Seattle, USA, Conf. on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00396"},{"key":"2099_CR28","unstructured":"Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. In: Proc, pp. 4470\u20134478. Long Beach, USA, Int. Conf. on Neural Information Processing Systems (2017)"},{"issue":"1","key":"2099_CR29","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"N Qian","year":"1999","unstructured":"Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145\u2013151 (1999)","journal-title":"Neural Netw."},{"issue":"16","key":"2099_CR30","doi-asserted-by":"publisher","first-page":"5608","DOI":"10.3390\/app10165608","volume":"10","author":"E Yaghoubi","year":"2020","unstructured":"Yaghoubi, E., Khezeli, F., Borza, D., Kumar, S.A., Neves, J., Proen\u00e7a, H.: Human attribute recognition: a comprehensive survey. Appl. Sci. 10(16), 5608 (2020)","journal-title":"Appl. Sci."},{"key":"2099_CR31","doi-asserted-by":"crossref","unstructured":"Polap, D., Wozniak, M.: Image features extractor based on hybridization of fuzzy controller and meta-heuristic. In: IEEE Int. Conference on Fuzzy Systems, Luxembourg, Luxembourg, pp. 1\u20136 (2021)","DOI":"10.1109\/FUZZ45933.2021.9494580"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-021-02099-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-021-02099-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-021-02099-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T11:12:55Z","timestamp":1659093175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-021-02099-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,20]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["2099"],"URL":"https:\/\/doi.org\/10.1007\/s11760-021-02099-7","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2022,1,20]]},"assertion":[{"value":"8 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}