{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T04:07:03Z","timestamp":1730520423519,"version":"3.28.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976188"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10489-023-05023-3","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T09:02:59Z","timestamp":1699002179000},"page":"29713-29722","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contrastive label assignment in vehicle detection"],"prefix":"10.1007","volume":"53","author":[{"given":"Erjun","family":"Sun","sequence":"first","affiliation":[]},{"given":"Di","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Zhaocheng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Xun","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,3]]},"reference":[{"issue":"1","key":"5023_CR1","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1109\/TITS.2020.3009000","volume":"23","author":"Y Tian","year":"2022","unstructured":"Tian Y, Chen T, Cheng G, Yu S, Li X, Li J, Yang B (2022) Global context assisted structure-aware vehicle retrieval. IEEE Trans Intell Transp Syst 23(1):165\u2013174","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5023_CR2","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.neucom.2020.09.068","volume":"433","author":"H Liu","year":"2021","unstructured":"Liu H, Nie H, Zhang Z, Li Y-F (2021) Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction. Neurocomputing 433:310\u2013322","journal-title":"Neurocomputing"},{"key":"5023_CR3","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1109\/TMM.2021.3081873","volume":"24","author":"H Liu","year":"2021","unstructured":"Liu H, Fang S, Zhang Z, Li D, Lin K, Wang J (2021) Mfdnet: collaborative poses perception and matrix fisher distribution for head pose estimation. IEEE Trans Multimedia 24:2449\u20132460","journal-title":"IEEE Trans Multimedia"},{"issue":"10","key":"5023_CR4","doi-asserted-by":"publisher","first-page":"7107","DOI":"10.1109\/TII.2022.3143605","volume":"18","author":"H Liu","year":"2022","unstructured":"Liu H, Liu T, Zhang Z, Sangaiah AK, Yang B, Li Y (2022) Arhpe: asymmetric relation-aware representation learning for head pose estimation in industrial human-computer interaction. IEEE Trans Industr Inf 18(10):7107\u20137117","journal-title":"IEEE Trans Industr Inf"},{"key":"5023_CR5","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neucom.2017.09.098","volume":"280","author":"Y Tian","year":"2018","unstructured":"Tian Y, Gelernter J, Wang X, Chen W, Gao J, Zhang Y, Li X (2018) Lane marking detection via deep convolutional neural network. Neurocomputing 280:46\u201355","journal-title":"Neurocomputing"},{"key":"5023_CR6","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2017.01.098","volume":"253","author":"Y Tian","year":"2017","unstructured":"Tian Y, Wang H, Wang X (2017) Object localization via evaluation multi-task learning. Neurocomputing 253:34\u201341","journal-title":"Neurocomputing"},{"key":"5023_CR7","unstructured":"Chen X, Wei F, Zeng G, et al (2022) Conditional detr v2: efficient detection transformer with box queries. arXiv:2207.08914"},{"key":"5023_CR8","unstructured":"Zhang H, Li F, Liu S, Su H, Zhu J, Ni LM, Shum H-Y (2023) Dino: Detr with improved denoising anchor boxes for end-to-end object detection. In: Proceedings of the international conference on learning representations, pp 460\u2013470"},{"key":"5023_CR9","doi-asserted-by":"crossref","unstructured":"Li F, Zhang H, Liu S, et al (2022) Dn-detr: accelerate detr training by introducing query denoising. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13619\u201313627","DOI":"10.1109\/CVPR52688.2022.01325"},{"key":"5023_CR10","unstructured":"Liu S, Li F, Zhang H, Yang X, Qi X, Su H, Zhu J, Zhang L (2022) Dab-detr: dynamic anchor boxes are better queries for detr. In: Proceedings of the international conference on learning representation, pp 213\u2013229"},{"key":"5023_CR11","doi-asserted-by":"publisher","first-page":"103711","DOI":"10.1016\/j.jvcir.2022.103711","volume":"90","author":"R Zhang","year":"2023","unstructured":"Zhang R, Tian Y, Xu Z, Liu D (2023) Design of anchor boxes and data augmentation for transformer-based vehicle localization. J Vis Commun Image Represent 90:103711","journal-title":"J Vis Commun Image Represent"},{"key":"5023_CR12","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhang X, Yang T, et al (2022) Anchor detr: query design for transformer-based object detection. In: Proceedings of the AAAI conference on artificial intelligence, pp 302\u2013311","DOI":"10.1609\/aaai.v36i3.20158"},{"key":"5023_CR13","unstructured":"Yang J, Li C, Zhang P, Dai X, Xiao B, Yuan L, Gao J (2021) Focal attention for long-range interactions in vision transformers. In: Proceedings of the advances in neural information processing systems, pp 2172\u20132180"},{"key":"5023_CR14","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"5023_CR15","doi-asserted-by":"crossref","unstructured":"Zhang S, Wang X, Wang J, Pang J, Lyu C, Zhang W, Luo P, Chen K (2023) Dense distinct query for end-to-end object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7329\u20137338","DOI":"10.1109\/CVPR52729.2023.00708"},{"key":"5023_CR16","unstructured":"Ouyang-Zhang J, Cho JH, Zhou X, Kr\u00e4henb\u00fchl P (2022) Nms strikes back. arXiv:2212.06137"},{"key":"5023_CR17","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1613\/jair.1.11338","volume":"64","author":"Y Tian","year":"2019","unstructured":"Tian Y, Wang X, Wu J, Wang R, Yang B (2019) Multi-scale hierarchical residual network for dense captioning. J Artif Intell Res 64:181\u2013196","journal-title":"J Artif Intell Res"},{"key":"5023_CR18","unstructured":"Chen Q, Chen X, Zeng G, Wang J (2022) Group detr: fast detr training with group-wise one-to-many label assignment. arXiv:2207.13085"},{"key":"5023_CR19","doi-asserted-by":"crossref","unstructured":"Zong Z, Song G, Liu Y (2023) Detrs with collaborative hybrid assignments training. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1009\u20131018","DOI":"10.1109\/ICCV51070.2023.00621"},{"key":"5023_CR20","doi-asserted-by":"crossref","unstructured":"Jia D, Yuan Y, He H, Wu X, Yu H, Lin W, Sun L, Zhang C, Hu H (2023) Detrs with hybrid matching. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 19702\u201319712","DOI":"10.1109\/CVPR52729.2023.01887"},{"key":"5023_CR21","doi-asserted-by":"crossref","unstructured":"Zhang R, Tian Y, Liu D (2022) Uncertainty region discovery and model refinement for domain adaptation in road detection. IEEE Intell Trans Syst Mag 2\u201310","DOI":"10.1109\/MITS.2022.3180892"},{"key":"5023_CR22","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: Proceedings of the European conference on computer vision, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"5023_CR23","doi-asserted-by":"crossref","unstructured":"Meng D, Chen X, Fan Z, et al (2021) Conditional detr for fast training convergence. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3651\u20133660","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"5023_CR24","doi-asserted-by":"crossref","unstructured":"Liu H, Liu T, Chen Y, Zhang Z, Li Y-F (2022) Ehpe: skeleton cues-based gaussian coordinate encoding for efficient human pose estimation. IEEE Trans Multimedia 1\u201312","DOI":"10.1109\/TMM.2022.3197364"},{"key":"5023_CR25","doi-asserted-by":"crossref","unstructured":"Liu T, Liu H, Yang B, Zhang Z (2023) Ldcnet: limb direction cuesaware network for flexible human pose estimation in industrial behavioral biometrics systems. IEEE Trans Industrial Inform 1\u201311","DOI":"10.1109\/TII.2023.3266366"},{"key":"5023_CR26","doi-asserted-by":"crossref","unstructured":"Liu H, Zhang C, Deng Y, Xie B, Liu T, Zhang Z, Li Y-F (2023) Transifc: invariant cues-aware feature concentration learning for efficient fine-grained bird image classification. IEEE Trans Multimedia 1\u201314","DOI":"10.1109\/TMM.2023.3238548"},{"issue":"12","key":"5023_CR27","doi-asserted-by":"publisher","first-page":"4466","DOI":"10.1109\/TITS.2018.2886283","volume":"20","author":"Y Tian","year":"2019","unstructured":"Tian Y, Gelernter J, Wang X, Li J, Yu Y (2019) Traffic sign detection using a multi-scale recurrent attention network. IEEE Trans Intell Transp Syst 20(12):4466\u20134475","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"1","key":"5023_CR28","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMECH.2018.2870056","volume":"24","author":"T Liu","year":"2018","unstructured":"Liu T, Liu H, Li Y, Zhang Z, Liu S (2018) Efficient blind signal reconstruction with wavelet transforms regularization for educational robot infrared vision sensing. IEEE\/ASME Trans Mechatron 24(1):384\u2013394","journal-title":"IEEE\/ASME Trans Mechatron"},{"issue":"1","key":"5023_CR29","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1109\/TII.2019.2934728","volume":"16","author":"T Liu","year":"2019","unstructured":"Liu T, Liu H, Li Y-F, Chen Z, Zhang Z, Liu S (2019) Flexible ftir spectral imaging enhancement for industrial robot infrared vision sensing. IEEE Trans Industr Inf 16(1):544\u2013554","journal-title":"IEEE Trans Industr Inf"},{"key":"5023_CR30","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.neucom.2020.12.090","volume":"436","author":"T Liu","year":"2021","unstructured":"Liu T, Wang J, Yang B, Wang X (2021) Ngdnet: Nonuniform gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom. Neurocomputing 436:210\u2013220","journal-title":"Neurocomputing"},{"key":"5023_CR31","doi-asserted-by":"crossref","unstructured":"Zhang H, Chang H, Ma B, Wang N, Chen X (2020) Dynamic r-cnn: towards high quality object detection via dynamic training. In: Proceedings of the european conference on computer vision, pp 260\u2013275","DOI":"10.1007\/978-3-030-58555-6_16"},{"key":"5023_CR32","doi-asserted-by":"crossref","unstructured":"Oksuz K, Cam BC, Akbas E, Kalkan S (2021) Rank & sort loss for object detection and instance segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3009\u20133018","DOI":"10.1109\/ICCV48922.2021.00300"},{"key":"5023_CR33","doi-asserted-by":"crossref","unstructured":"Kim K, Lee, HS (2020) Probabilistic anchor assignment with iou prediction for object detection. In: Proceedings of the european conference on computer vision, pp 355\u2013371","DOI":"10.1007\/978-3-030-58595-2_22"},{"key":"5023_CR34","doi-asserted-by":"crossref","unstructured":"Li S, He C, Li R, Zhang L (2022) A dual weighting label assignment scheme for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9387\u20139396","DOI":"10.1109\/CVPR52688.2022.00917"},{"key":"5023_CR35","doi-asserted-by":"crossref","unstructured":"Li S, Li M, Li R, He C, Zhang L (2023) One-to-few label assignment for end-to-end dense detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7350\u20137359","DOI":"10.1109\/CVPR52729.2023.00710"},{"issue":"4","key":"5023_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3504033","volume":"18","author":"Y Tian","year":"2022","unstructured":"Tian Y, Zhang Y, Chen W-G, Liu D, Wang H, Xu H, Han J, Ge Y (2022) 3d tooth instance segmentation learning objectness and affinity in point cloud. ACM Trans Multimed Comput Commun Appl 18(4):1\u201316","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"5023_CR37","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez, AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Proceedings of the advances in neural information processing Systems"},{"key":"5023_CR38","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Proceedings of the advances in neural information processing systems, pp 1009\u20131018"},{"key":"5023_CR39","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al (2019) Pytorch: an imperative style, high-performance deep learning library. In: Proceedings of the advances in neural information processing systems, pp 759\u2013768"},{"key":"5023_CR40","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the international conference on artificial intelligence and statistics, pp 249\u2013256"},{"key":"5023_CR41","doi-asserted-by":"crossref","unstructured":"Lin, T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"5023_CR42","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"5023_CR43","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5023_CR44","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"5023_CR45","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.neucom.2019.01.104","volume":"347","author":"Y Tian","year":"2019","unstructured":"Tian Y, Hu W, Jiang H, Wu J (2019) Densely connected attentional pyramid residual network for human pose estimation. Neurocomputing 347:13\u201323","journal-title":"Neurocomputing"},{"issue":"6","key":"5023_CR46","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1049\/iet-cvi.2018.5492","volume":"13","author":"Y Tian","year":"2019","unstructured":"Tian Y, Cao Y, Wu J, Hu W, Song C, Yang T (2019) Multi-cue combination network for action-based video classification. IET Comput Vision 13(6):542\u2013548","journal-title":"IET Comput Vision"},{"key":"5023_CR47","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neucom.2020.07.078","volume":"417","author":"Y Tian","year":"2020","unstructured":"Tian Y, Zhang Y, Zhou D, Cheng G, Chen W-G, Wang R (2020) Triple attention network for video segmentation. Neurocomputing 417:202\u2013211","journal-title":"Neurocomputing"},{"key":"5023_CR48","doi-asserted-by":"crossref","unstructured":"Tian Y, Cheng G, Gelernter J, Yu S, Song C, Yang B (2020) Joint temporal context exploitation and active learning for video segmentation. Pattern Recogn 100:107158","DOI":"10.1016\/j.patcog.2019.107158"},{"issue":"22","key":"5023_CR49","doi-asserted-by":"publisher","first-page":"20285","DOI":"10.1007\/s00521-022-07578-7","volume":"34","author":"D Liu","year":"2022","unstructured":"Liu D, Tian Y, Xu Z, Jian G (2022) Handling occlusion in prohibited item detection from x-ray images. Neural Comput Appl 34(22):20285\u201320298","journal-title":"Neural Comput Appl"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05023-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05023-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05023-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T09:16:45Z","timestamp":1730452605000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05023-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,3]]},"references-count":49,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["5023"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05023-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,11,3]]},"assertion":[{"value":"18 September 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\/Competing interests"}}]}}