{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:04:23Z","timestamp":1787317463165,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":77,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031733895","type":"print"},{"value":"9783031733901","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73390-1_10","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T12:24:01Z","timestamp":1730291041000},"page":"161-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Projecting Points to\u00a0Axes: Oriented Object Detection via\u00a0Point-Axis Representation"],"prefix":"10.1007","author":[{"given":"Zeyang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qilong","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihong","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Xia, G.-S., et al.: A large-scale dataset for object detection in aerial images. In: CVPR, Dota (2018)","DOI":"10.1109\/CVPR.2018.00418"},{"issue":"11","key":"10_CR2","doi-asserted-by":"publisher","first-page":"7778","DOI":"10.1109\/TPAMI.2021.3117983","volume":"44","author":"J Ding","year":"2021","unstructured":"Ding, J., et al.: Object detection in aerial images: a large-scale benchmark and challenges. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7778\u20137796 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR3","first-page":"1","volume":"60","author":"G Cheng","year":"2022","unstructured":"Cheng, G., Wang, J., Li, K., Xie, X., Lang, C., Yao, Y., Han, J.: Anchor-free oriented proposal generator for object detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201311 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et\u00a0al.: Mmrotate: a rotated object detection benchmark using pytorch. In: ACM MM (2022)","DOI":"10.1145\/3503161.3548541"},{"issue":"2","key":"10_CR5","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3390\/rs16020327","volume":"16","author":"S Gui","year":"2024","unstructured":"Gui, S., Song, S., Qin, R., Tang, Y.: Remote sensing object detection in the deep learning era\u2013a review. Remote Sensing 16(2), 327 (2024)","journal-title":"Remote Sensing"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: Unified, real-time object detection. In: CVPR, You only look once (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: Fully convolutional one-stage object detection. In ICCV, Fcos (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. Proceedings of the IEEE (2023)","DOI":"10.1109\/JPROC.2023.3238524"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Song, X., He, Y, Dong, S., Gong, Y.: Non-exemplar domain incremental object detection via learning domain bias. In: AAAI (2024)","DOI":"10.1609\/aaai.v38i13.29427"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.-S., Lu, O.: Learning roi transformer for oriented object detection in aerial images. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00296"},{"key":"10_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/978-3-030-58598-3_40","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Yang","year":"2020","unstructured":"Yang, X., Yan, J.: Arbitrary-oriented object detection with circular smooth label. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 677\u2013694. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_40"},{"key":"10_CR14","unstructured":"Yang, X., et al.: The kfiou loss for rotated object detection. In: ICLR (2023)"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Yu, Y., Da, F.: Phase-shifting coder: Predicting accurate orientation in oriented object detection. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01283"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Qian, W., Yang, X., Peng, S., Yan, J., Guo, Y.: Learning modulated loss for rotated object detection. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i3.16347"},{"issue":"4","key":"10_CR17","first-page":"1452","volume":"43","author":"X Yongchao","year":"2020","unstructured":"Yongchao, X., et al.: Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1452\u20131459 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3115110","volume":"19","author":"Q Ming","year":"2021","unstructured":"Ming, Q., Miao, L., Zhou, Z., Yang, X., Dong, Y.: Optimization for arbitrary-oriented object detection via representation invariance loss. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2021)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., Metaxas, D.: Oriented object detection in aerial images with box boundary-aware vectors. In: WACV (2021)","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.isprsjprs.2020.09.022","volume":"169","author":"H Wei","year":"2020","unstructured":"Wei, H., Zhang, Y., Chang, Z., Li, H., Wang, H., Sun, X.: Oriented objects as pairs of middle lines. ISPRS J. Photogramm. Remote. Sens. 169, 268\u2013279 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"10_CR21","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., Tian, Q.: Rethinking rotated object detection with gaussian wasserstein distance loss. In: ICML (2021)"},{"key":"10_CR22","unstructured":"Yang, X., et al.: Learning high-precision bounding box for rotated object detection via kullback-leibler divergence. NeurIPS (2021)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Yu, Y., Da, F.: On boundary discontinuity in angle regression based arbitrary oriented object detection. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3378777"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Yang, G., Yang, X., Mu, T., Yan, J., Hu, S.: Theoretically achieving continuous representation of oriented bounding boxes. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01600"},{"key":"10_CR25","unstructured":"Chen, Y., Zhang, Z., Cao, Y., Wang, L., Lin, S., Hu, H.: Reppoints v2: Verification meets regression for object detection. NeurIPS (2020)"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Li, W., Chen, Y., Hu, K., Zhu, J.: Oriented reppoints for aerial object detection. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00187"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., Han, J.: Oriented r-cnn for object detection. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., Xia, G.-S.: A rotation-equivariant detector for aerial object detection. In: CVPR, Redet (2021)","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Pu, Y., et al.: Adaptive rotated convolution for rotated object detection. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00606"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M.-M., Yang, J., Li, X.: Large selective kernel network for remote sensing object detection. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Yang, X., Hou, L., Zhou, Y., Wang, W., Yan, J.: Dense label encoding for boundary discontinuity free rotation detection. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"10_CR32","unstructured":"Dongchen, L., Li, D., Li, Y., Wang, S.: Orientation-sensitive keypoint localization for rotated object detection. In CVPR, Oskdet (2022)"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Yu, H., Tian, Y., Ye, Q., Liu, Y.: Spatial transform decoupling for oriented object detection. In: AAAI (2024)","DOI":"10.1609\/aaai.v38i7.28502"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: The devil is in the crack orientation: a new perspective for crack detection. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00612"},{"key":"10_CR35","unstructured":"Vaswani, A., et al.: Attention is all you need. NeurIPS (2017)"},{"key":"10_CR36","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"10_CR37","unstructured":"Wei, X., et al.: Scene-adaptive attention network for crowd counting. arXiv preprint arXiv:2112.15509 (2021)"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Shi, D., Wei, X., Li, L., Ren, Y., Tan, W.: End-to-end multi-person pose estimation with transformers. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01079"},{"key":"10_CR39","doi-asserted-by":"crossref","unstructured":"Wei, X., Bai, Y., Zheng, Y., Shi, D., Gong, Y.: Autoregressive visual tracking. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00935"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Bai, Y., Zhao, Z., Gong, Y., Wei, X.: Artrackv2: Prompting autoregressive tracker where to look and how to describe. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01802"},{"key":"10_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"10_CR42","unstructured":"Zhu, X., Weijie, S., Lewei, L., Li, B., Wang, X., Dai, J.: Deformable transformers for end-to-end object detection. In: ICLR, Deformable detr (2021)"},{"key":"10_CR43","unstructured":"Liu, S., et al.: Dynamic anchor boxes are better queries for detr. In: ICLR, Dab-detr (2022)"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, G., Luo, Z., Yu, Y., Cui, K., Lu, S.: Accelerating detr convergence via semantic-aligned matching. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00102"},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, G., Luo, Z., Tian, Z., Zhang, J., Zhang, X., Lu, S.: Towards efficient use of multi-scale features in transformer-based object detectors. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00601"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: delving into high quality object detection. In VPR (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Dai, X., et al.: Unifying object detection heads with attentions. In: CVPR, Dynamic head (2021)","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"10_CR48","unstructured":"Zhang, H., et al.: Detr with improved denoising anchor boxes for end-to-end object detection. In: ICLR (2023)"},{"key":"10_CR49","doi-asserted-by":"crossref","unstructured":"Zong, Z., Song, G., Liu, Y.: Detrs with collaborative hybrid assignments training. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00621"},{"key":"10_CR50","doi-asserted-by":"crossref","unstructured":"Liu, S., et\u00a0al.: Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv (2023)","DOI":"10.1007\/978-3-031-72970-6_3"},{"key":"10_CR51","doi-asserted-by":"crossref","unstructured":"Li, F., et al.: Mask dino: Towards a unified transformer-based framework for object detection and segmentation. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00297"},{"key":"10_CR52","unstructured":"Li, F., et al.: Visual in-context prompting. arXiv, (2023)"},{"key":"10_CR53","doi-asserted-by":"crossref","unstructured":"Hu, Z., et al.: Emo2-detr: efficient-matching oriented object detection with transformers. IEEE Trans. Geosci. Remote Sensing (2023)","DOI":"10.1109\/TGRS.2023.3300154"},{"issue":"5","key":"10_CR54","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.1109\/TCSVT.2022.3222906","volume":"33","author":"L Dai","year":"2022","unstructured":"Dai, L., Liu, H., Tang, H., Zhiwei, W., Song, P.: Ao2-detr: arbitrary-oriented object detection transformer. IEEE Trans. Circuits Syst. Video Technol. 33(5), 2342\u20132356 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3478055","volume":"62","author":"Y Zeng","year":"2024","unstructured":"Zeng, Y., Chen, Y., Yang, X., Li, Q., Yan, J.: Ars-detr: aspect ratio-sensitive detection transformer for aerial oriented object detection. IEEE Trans. Geosci. Remote Sens. 62, 1\u201315 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR56","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","volume":"159","author":"K Li","year":"2020","unstructured":"Li, K., Wan, G., Cheng, G., Meng, L., Han, J.: Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS J. Photogramm. Remote. Sens. 159, 296\u2013307 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"10_CR57","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yuan, L., Weng, L.,Yang, Y.: A high resolution optical satellite image dataset for ship recognition and some new baselines. In: International Conference on Pattern Recognition Applications and Methods, vol. 2, pp. 324\u2013331. SciTePress (2017)","DOI":"10.5220\/0006120603240331"},{"key":"10_CR58","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., et al.: Common objects in context. In ECCV, Microsoft coco (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"10_CR59","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR60","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Hierarchical vision transformer using shifted windows. In ICCV, Swin transformer (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"2","key":"10_CR61","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Winn, J.: The pascal visual object classes challenge 2007 (voc2007) development kit. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"10_CR62","unstructured":"Everingham, M., Winn, J.:The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep 2007(1-45):5 (2012)"},{"key":"10_CR63","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Feng, Z., He, T.: R3det: refined single-stage detector with feature refinement for rotating object. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"10_CR64","doi-asserted-by":"crossref","unstructured":"Hou, L., Lu, K., Xue, J., Li, Y.: Shape-adaptive selection and measurement for oriented object detection. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i1.19975"},{"key":"10_CR65","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1109\/TIP.2022.3148874","volume":"31","author":"Z Huang","year":"2022","unstructured":"Huang, Z., Li, W., Xia, X.-G., Tao, R.: A general gaussian heatmap label assignment for arbitrary-oriented object detection. IEEE Trans. Image Process. 31, 1895\u20131910 (2022)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"10_CR66","first-page":"4932","volume":"45","author":"G Nie","year":"2023","unstructured":"Nie, G., Huang, H.: Multi-oriented object detection in aerial images with double horizontal rectangles. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4932\u20134944 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR67","doi-asserted-by":"crossref","unstructured":"Xu, C., et al.: Dynamic coarse-to-fine learning for oriented tiny object detection. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00707"},{"key":"10_CR68","doi-asserted-by":"crossref","unstructured":"Yang, X., et al.: Scrdet: Towards more robust detection for small, cluttered and rotated objects. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00832"},{"key":"10_CR69","first-page":"1","volume":"61","author":"D Wang","year":"2022","unstructured":"Wang, D., et al.: Advancing plain vision transformer toward remote sensing foundation model. IEEE Trans. Geosci. Remote Sens. 61, 1\u201315 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR70","doi-asserted-by":"crossref","unstructured":"Cai, X., Lai, Q., Wang, Y., Wang, W., Sun, Z., Yao, Y.: Poly kernel inception network for remote sensing detection. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.02617"},{"key":"10_CR71","first-page":"1","volume":"60","author":"G Cheng","year":"2022","unstructured":"Cheng, G., et al.: Anchor-free oriented proposal generator for object detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201311 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR72","unstructured":"Lyu, C., et al.: An empirical study of designing real-time object detectors. arXiv, Rtmdet (2022)"},{"key":"10_CR73","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.isprsjprs.2023.01.001","volume":"196","author":"Q Ming","year":"2023","unstructured":"Ming, Q., Miao, L., Zhou, Z., Song, J., Dong, Y., Yang, X.: Task interleaving and orientation estimation for high-precision oriented object detection in aerial images. ISPRS J. Photogramm. Remote. Sens. 196, 241\u2013255 (2023)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"4","key":"10_CR74","first-page":"4335","volume":"45","author":"X Yang","year":"2022","unstructured":"Yang, X., et al.: Detecting rotated objects as gaussian distributions and its 3-d generalization. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4335\u20134354 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR75","doi-asserted-by":"crossref","unstructured":"Li, F., Zhang, H., Liu, S., Guo, J., Ni, L.M., Zhang, L.: Accelerate detr training by introducing query denoising. In: CVPR, Dn-detr (2022)","DOI":"10.1109\/CVPR52688.2022.01325"},{"key":"10_CR76","doi-asserted-by":"crossref","unstructured":"Jia, D., et al.: Detrs with hybrid matching. In: CVPR, Lei Sun (2023)","DOI":"10.1109\/CVPR52729.2023.01887"},{"key":"10_CR77","unstructured":"Ren, T., et\u00a0al.: detrex: Benchmarking detection transformers. arXiv (2023)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73390-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T09:29:32Z","timestamp":1732958972000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73390-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031733895","9783031733901"],"references-count":77,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73390-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}