{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:19:28Z","timestamp":1743045568737,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031554704"},{"type":"electronic","value":"9783031554711"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-55471-1_17","type":"book-chapter","created":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T05:02:22Z","timestamp":1710565342000},"page":"227-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Pedestrian Attribute Recognition with Dense Feature Pyramid and Mixed Pooling"],"prefix":"10.1007","author":[{"given":"He","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Chen","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Yaosheng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Sujia","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Siwen","family":"Dong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,17]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Liu, W., Liao, S., Ren, W., Hu, W., Yu, Y.: High-level semantic feature detection: a new perspective for pedestrian detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5187\u20135196 (2019)","DOI":"10.1109\/CVPR.2019.00533"},{"key":"17_CR2","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.patcog.2019.06.006","volume":"95","author":"Y Lin","year":"2019","unstructured":"Lin, Y., et al.: Improving person re-identification by attribute and identity learning. Pattern Recognit. 95, 151\u2013161 (2019)","journal-title":"Pattern Recognit."},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.neucom.2020.03.057","volume":"402","author":"Y Shi","year":"2020","unstructured":"Shi, Y., Ling, H., Wu, L., Shen, J., Li, P.: Learning refined attribute-aligned network with attribute selection for person re-identification. Neurocomputing 402, 124\u2013133 (2020)","journal-title":"Neurocomputing"},{"issue":"4","key":"17_CR4","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1109\/TIP.2018.2878349","volume":"28","author":"D Li","year":"2018","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 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.neucom.2018.01.092","volume":"300","author":"A Brunetti","year":"2018","unstructured":"Brunetti, A., Buongiorno, D., Trotta, G.F., Bevilacqua, V.: Computer vision and deep learning techniques for pedestrian detection and tracking: a survey. Neurocomputing 300, 17\u201333 (2018)","journal-title":"Neurocomputing"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Tay, C.P., Roy, S., Yap, K.H.: AANet: attribute attention network for person re-identifications. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7134\u20137143 (2019)","DOI":"10.1109\/CVPR.2019.00730"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Deng, W., Wang, S.: SVDNet for pedestrian retrieval. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3800\u20133808 (2017)","DOI":"10.1109\/ICCV.2017.410"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, X., Huang, K.: Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 111\u2013115. IEEE, November 2015","DOI":"10.1109\/ACPR.2015.7486476"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, X., Zhang, Z., Huang, K.: Pose guided deep model for pedestrian attribute recognition in surveillance scenarios. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE, July 2018","DOI":"10.1109\/ICME.2018.8486604"},{"key":"17_CR10","unstructured":"Liu, P., Liu, X., Yan, J., Shao, J.: Localization guided learning for pedestrian attribute recognition (2018). arXiv preprint arXiv:1808.09102"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: HydraPlus-Net: attentive deep features for pedestrian analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 350\u2013359 (2017)","DOI":"10.1109\/ICCV.2017.46"},{"key":"17_CR12","unstructured":"Sarfraz, M.S., Schumann, A., Wang, Y., Stiefelhagen, R.: Deep view-sensitive pedestrian attribute inference in an end-to-end model (2017). arXiv preprint arXiv:1707.06089"},{"key":"17_CR13","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.patrec.2017.05.012","volume":"94","author":"H Guo","year":"2017","unstructured":"Guo, H., Fan, X., Wang, S.: Human attribute recognition by refining attention heat map. Pattern Recognit. Lett. 94, 38\u201345 (2017)","journal-title":"Pattern Recognit. Lett."},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhu, X., Gong, S., Li, W.: Attribute recognition by joint recurrent learning of context and correlation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 531\u2013540 (2017)","DOI":"10.1109\/ICCV.2017.65"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, X., Sang, L., Ding, G., Han, J., Di, N., Yan, C.: Recurrent attention model for pedestrian attribute recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 9275\u20139282, July 2019","DOI":"10.1609\/aaai.v33i01.33019275"},{"key":"17_CR16","unstructured":"Yu, K., Leng, B., Zhang, Z., Li, D., Huang, K.: Weakly-supervised learning of mid-level features for pedestrian attribute recognition and localization (2016). arXiv preprint arXiv:1611.05603"},{"key":"17_CR17","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 29 (2016)"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Honari, S., Yosinski, J., Vincent, P., Pal, C.: Recombinator networks: learning coarse-to-fine feature aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5743\u20135752 (2016)","DOI":"10.1109\/CVPR.2016.619"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Deng, Y., Luo, P., Loy, C.C., Tang, X.: Pedestrian attribute recognition at far distance. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 789\u2013792, November 2014","DOI":"10.1145\/2647868.2654966"},{"key":"17_CR21","unstructured":"Li, D., Zhang, Z., Chen, X., Ling, H., Huang, K.: A richly annotated dataset for pedestrian attribute recognition (2016). arXiv preprint arXiv:1603.07054"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: DenseASPP for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3684\u20133692 (2018)","DOI":"10.1109\/CVPR.2018.00388"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Quan, Y., Zhang, D., Zhang, L., Tang, J.: Centralized Feature Pyramid for Object Detection (2022). arXiv preprint arXiv:2210.02093","DOI":"10.1109\/TIP.2023.3297408"},{"key":"17_CR26","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017). arXiv preprint arXiv:1706.05587"},{"key":"17_CR27","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs (2016). arXiv preprint arXiv:1606.00915"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jia, J., Koltun, V.: Exploring self-attention for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10076\u201310085 (2020)","DOI":"10.1109\/CVPR42600.2020.01009"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: CCNET: criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 603\u2013612 (2019)","DOI":"10.1109\/ICCV.2019.00069"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Guo, Y., Li, Y., Wang, L., Rosing, T.: Depthwise convolution is all you need for learning multiple visual domains. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 8368\u20138375, July 2019","DOI":"10.1609\/aaai.v33i01.33018368"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhang, L., Cheng, M.M., Feng, J.: Strip pooling: rethinking spatial pooling for scene parsing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4003\u20134012 (2020)","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Sudowe, P., Spitzer, H., Leibe, B.: Person attribute recognition with a jointly-trained holistic CNN model. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 87\u201395 (2015)","DOI":"10.1109\/ICCVW.2015.51"},{"key":"17_CR34","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: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE, July 2020","DOI":"10.1109\/ICME46284.2020.9102757"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Di, X., Zhang, H., Patel, V.M.: Polarimetric thermal to visible face verification via attribute preserved synthesis. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1\u201310. IEEE, October 2018","DOI":"10.1109\/BTAS.2018.8698554"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"Tang, C., Sheng, L., Zhang, Z., Hu, X.: Improving pedestrian attribute recognition with weakly-supervised multi-scale attribute-specific localization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4997\u20135006 (2019)","DOI":"10.1109\/ICCV.2019.00510"},{"key":"17_CR37","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR, June 2015"},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Zhong, J., Qiao, H., Chen, L., Shang, M., Liu, Q.: Improving pedestrian attribute recognition with multi-scale spatial calibration. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE, July 2021","DOI":"10.1109\/IJCNN52387.2021.9533647"},{"key":"17_CR39","unstructured":"Jia, J., Huang, H., Yang, W., Chen, X., Huang, K.: Rethinking of pedestrian attribute recognition: Realistic datasets with efficient method (2020). arXiv preprint arXiv:2005.11909"},{"key":"17_CR40","unstructured":"Zou, C., Xie, W., Xie, X., Zhao, K., Liu, Q., Xiao, H.: Pedestrian Attribute Recognition Based on Multi-Scale Feature Fusion Over a Larger Receptive Field and Strip Pooling (2022)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Mobile Networks and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-55471-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T05:06:59Z","timestamp":1710565619000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-55471-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031554704","9783031554711"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-55471-1_17","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MONAMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mobile Networks and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yingtan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"monami2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mon-ami.eai-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}