{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:15:02Z","timestamp":1775808902664,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"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":["62371015"],"award-info":[{"award-number":["62371015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471013"],"award-info":[{"award-number":["62471013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["L211017"],"award-info":[{"award-number":["L211017"]}]},{"name":"Beijing Natural Science Foundation","award":["L247025"],"award-info":[{"award-number":["L247025"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s13042-025-02586-0","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:19:01Z","timestamp":1740961141000},"page":"5589-5604","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Coarse-to-fine domain adaptation object detection with feature disentanglement"],"prefix":"10.1007","volume":"16","author":[{"given":"Jiafeng","family":"Li","sequence":"first","affiliation":[]},{"given":"Mengxun","family":"Zhi","sequence":"additional","affiliation":[]},{"given":"Yongyu","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Li","family":"Zhuo","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"2586_CR1","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430"},{"key":"2586_CR2","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28"},{"key":"2586_CR3","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM (2023) Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"2586_CR4","doi-asserted-by":"crossref","unstructured":"Chen Y, Li W, Sakaridis C, Dai D, Van Gool L (2018) Domain adaptive faster r-cnn for object detection in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3339\u20133348","DOI":"10.1109\/CVPR.2018.00352"},{"key":"2586_CR5","doi-asserted-by":"crossref","unstructured":"Liu D, Zhang C, Song Y, Huang H, Wang C, Barnett M, Cai W (2022) Decompose to adapt: Cross-domain object detection via feature disentanglement. IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2022.3141614"},{"key":"2586_CR6","doi-asserted-by":"crossref","unstructured":"Vs V, Gupta V, Oza P, Sindagi VA, Patel VM (2021) Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4516\u20134526","DOI":"10.1109\/CVPR46437.2021.00449"},{"key":"2586_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116334","volume":"192","author":"VF Arruda","year":"2022","unstructured":"Arruda VF, Berriel RF, Paix\u00e3o TM, Badue C, De Souza AF, Sebe N, Oliveira-Santos T (2022) Cross-domain object detection using unsupervised image translation. Expert Syst Appl 192:116334","journal-title":"Expert Syst Appl"},{"key":"2586_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117697","volume":"205","author":"M Do","year":"2022","unstructured":"Do M, Jeon S, Lee P, Hong K, Ma Y-S, Byun H (2022) Exploiting domain transferability for collaborative inter-level domain adaptive object detection. Expert Syst Appl 205:117697","journal-title":"Expert Syst Appl"},{"key":"2586_CR9","doi-asserted-by":"publisher","unstructured":"Chen C, Li J, Zhou H-Y, Han X, Huang Y, Ding X, Yu Y (2022) Relation matters: Foreground-aware graph-based relational reasoning for domain adaptive object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1\u20131. https:\/\/doi.org\/10.1109\/TPAMI.2022.3179445","DOI":"10.1109\/TPAMI.2022.3179445"},{"key":"2586_CR10","doi-asserted-by":"crossref","unstructured":"Saito K, Ushiku Y, Harada T, Saenko K (2019) Strong-weak distribution alignment for adaptive object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6956\u20136965","DOI":"10.1109\/CVPR.2019.00712"},{"key":"2586_CR11","doi-asserted-by":"crossref","unstructured":"Inoue N, Furuta R, Yamasaki T, Aizawa, K (2018) Cross-domain weakly-supervised object detection through progressive domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5001\u20135009","DOI":"10.1109\/CVPR.2018.00525"},{"issue":"8","key":"2586_CR12","doi-asserted-by":"publisher","first-page":"12633","DOI":"10.1109\/TITS.2021.3115823","volume":"23","author":"H Zhang","year":"2022","unstructured":"Zhang H, Luo G, Li J, Wang F-Y (2022) C2fda: Coarse-to-fine domain adaptation for traffic object detection. IEEE Trans Intell Transp Syst 23(8):12633\u201312647. https:\/\/doi.org\/10.1109\/TITS.2021.3115823","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2586_CR13","doi-asserted-by":"publisher","first-page":"2502","DOI":"10.1109\/TMM.2021.3082687","volume":"24","author":"D Guan","year":"2022","unstructured":"Guan D, Huang J, Xiao A, Lu S, Cao Y (2022) Uncertainty-aware unsupervised domain adaptation in object detection. IEEE Trans Multimedia 24:2502\u20132514. https:\/\/doi.org\/10.1109\/TMM.2021.3082687","journal-title":"IEEE Trans Multimedia"},{"key":"2586_CR14","doi-asserted-by":"crossref","unstructured":"Li G, Ji Z, Qu X, Zhou R, Cao D (2022) Cross-domain object detection for autonomous driving: A stepwise domain adaptative yolo approach. IEEE Transactions on Intelligent Vehicles","DOI":"10.1109\/TIV.2022.3165353"},{"key":"2586_CR15","doi-asserted-by":"crossref","unstructured":"Hsu C-C, Tsai Y-H, Lin Y-Y, Yang M-H (2020) Every pixel matters: Center-aware feature alignment for domain adaptive object detector. In: European Conference on Computer Vision, pp. 733\u2013748. Springer","DOI":"10.1007\/978-3-030-58545-7_42"},{"key":"2586_CR16","unstructured":"Jocher GR, Stoken A, Borovec J, NanoCode Chaurasia A, TaoXie Changyu L., Abhiram Laughing tkianai yxNONG Hogan A, lorenzomammana AlexWang H\u00e1jek J, Diaconu L, Marc Kwon Y, Oleg wanghaoyang Defretin Y, Lohia A, ah Milanko B, Fineran B, Khromov DP, Yiwei D, Doug Durgesh Ingham F (2021) ultralytics\/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations"},{"key":"2586_CR17","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"2586_CR18","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2019) Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9627\u20139636","DOI":"10.1109\/ICCV.2019.00972"},{"issue":"11","key":"2586_CR19","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"2586_CR20","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214\u2013223. PMLR"},{"key":"2586_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107046","volume":"224","author":"L Wen","year":"2021","unstructured":"Wen L, Bai H, He L, Zhou Y, Zhou M, Xu Z (2021) Gradient estimation of information measures in deep learning. Knowl-Based Syst 224:107046","journal-title":"Knowl-Based Syst"},{"key":"2586_CR22","unstructured":"Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055"},{"key":"2586_CR23","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"2586_CR24","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: European Conference on Computer Vision, pp. 21\u201337. Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2586_CR25","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"2586_CR26","unstructured":"Farhadi A, Redmon J (2018) Yolov3: An incremental improvement. In: Computer Vision and Pattern Recognition, vol. 1804, pp. 1\u20136. Springer Berlin\/Heidelberg, Germany"},{"key":"2586_CR27","unstructured":"Zhou X, Wang D, Kr\u00e4henb\u00fchl P (2019) Objects as points. arXiv preprint arXiv:1904.07850"},{"key":"2586_CR28","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"2586_CR29","unstructured":"Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W, et al (2022) Yolov6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976"},{"key":"2586_CR30","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Yeh I-H, Liao H-YM (2024) Yolov9: Learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"2586_CR31","unstructured":"Wang A, Chen H, Liu L, Chen K, Lin Z, Han J, Ding G (2024) Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458"},{"key":"2586_CR32","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2016) Deep coral: Correlation alignment for deep domain adaptation. In: European Conference on Computer Vision, pp. 443\u2013450. Springer","DOI":"10.1007\/978-3-319-49409-8_35"},{"issue":"14","key":"2586_CR33","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/bioinformatics\/btl242","volume":"22","author":"KM Borgwardt","year":"2006","unstructured":"Borgwardt KM, Gretton A, Rasch MJ, Kriegel H-P, Sch\u00f6lkopf B, Smola AJ (2006) Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14):49\u201357","journal-title":"Bioinformatics"},{"key":"2586_CR34","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474"},{"key":"2586_CR35","doi-asserted-by":"crossref","unstructured":"Inoue N, Furuta R, Yamasaki T, Aizawa K (2018) Cross-domain weakly-supervised object detection through progressive domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5001\u20135009","DOI":"10.1109\/CVPR.2018.00525"},{"key":"2586_CR36","doi-asserted-by":"crossref","unstructured":"Hsu H-K, Yao C-H, Tsai Y-H, Hung W-C, Tseng H-Y, Singh M, Yang M-H (2020) Progressive domain adaptation for object detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 749\u2013757","DOI":"10.1109\/WACV45572.2020.9093358"},{"key":"2586_CR37","doi-asserted-by":"crossref","unstructured":"Huang J, Guan D, Xiao A, Lu S (2021) Cross-view regularization for domain adaptive panoptic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10133\u201310144","DOI":"10.1109\/CVPR46437.2021.01000"},{"key":"2586_CR38","doi-asserted-by":"crossref","unstructured":"Toldo M, Michieli U, Zanuttigh P (2024) Learning with style: Continual semantic segmentation across tasks and domains. IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2024.3397461"},{"key":"2586_CR39","doi-asserted-by":"crossref","unstructured":"Kim S, Choi J, Kim T, Kim C (2019) Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6092\u20136101","DOI":"10.1109\/ICCV.2019.00619"},{"key":"2586_CR40","doi-asserted-by":"crossref","unstructured":"Zhang P, Zhang B, Zhang T, Chen D, Wang Y, Wen F (2021) Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12414\u201312424","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"2586_CR41","doi-asserted-by":"crossref","unstructured":"Wen C, Zhao F, Liu W (2024) Unsupervised domain specificity for knowledge transfer. International Journal of Machine Learning and Cybernetics, 1\u201311","DOI":"10.1007\/s13042-024-02165-9"},{"issue":"8","key":"2586_CR42","doi-asserted-by":"publisher","first-page":"10613","DOI":"10.1007\/s11063-023-11341-x","volume":"55","author":"Y Zheng","year":"2023","unstructured":"Zheng Y, He L, Wu X, Pan C (2023) Self-training and multi-level adversarial network for domain adaptive remote sensing image segmentation. Neural Process Lett 55(8):10613\u201310638","journal-title":"Neural Process Lett"},{"key":"2586_CR43","doi-asserted-by":"crossref","unstructured":"Munir MA, Khan MH, Sarfraz MS, Ali M (2023) Domain adaptive object detection via balancing between self-training and adversarial learning. IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2023.3290135"},{"key":"2586_CR44","doi-asserted-by":"crossref","unstructured":"Gao K, Yu A, You X, Guo W, Li K, Huang N (2023) Integrating multiple sources knowledge for class asymmetry domain adaptation segmentation of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing","DOI":"10.1109\/TGRS.2023.3345159"},{"issue":"5","key":"2586_CR45","first-page":"6354","volume":"45","author":"J Yang","year":"2022","unstructured":"Yang J, Shi S, Wang Z, Li H, Qi X (2022) St3d++: Denoised self-training for unsupervised domain adaptation on 3d object detection. IEEE Trans Pattern Anal Mach Intell 45(5):6354\u20136371","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2586_CR46","doi-asserted-by":"crossref","unstructured":"Chen Y-Y, Jhong S-Y (2023) Multilevel self-training approach for cross-domain semantic segmentation in intelligent vehicles. IEEE Intelligent Transportation Systems Magazine","DOI":"10.1109\/MITS.2023.3310027"},{"key":"2586_CR47","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180\u20131189. PMLR"},{"key":"2586_CR48","unstructured":"Ozyurt Y, Feuerriegel S, Zhang C (2022) Contrastive learning for unsupervised domain adaptation of time series. arXiv preprint arXiv:2206.06243"},{"key":"2586_CR49","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhou C, Huang D (2024) Stal3d: Unsupervised domain adaptation for 3d object detection via collaborating self-training and adversarial learning. IEEE Transactions on Intelligent Vehicles","DOI":"10.1109\/TIV.2024.3397194"},{"issue":"6","key":"2586_CR50","doi-asserted-by":"publisher","first-page":"2367","DOI":"10.1007\/s13042-023-02035-w","volume":"15","author":"J Lin","year":"2024","unstructured":"Lin J, Bian Z, Wang S (2024) Deep adversarial reconstruction classification network for unsupervised domain adaptation. Int J Mach Learn Cybern 15(6):2367\u20132382","journal-title":"Int J Mach Learn Cybern"},{"key":"2586_CR51","doi-asserted-by":"crossref","unstructured":"Sun T, Lu C, Ling H (2023) Domain adaptation with adversarial training on penultimate activations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 9935\u20139943","DOI":"10.1609\/aaai.v37i8.26185"},{"key":"2586_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104208","volume":"100","author":"D Zhang","year":"2021","unstructured":"Zhang D, Li J, Li X, Du Z, Xiong L, Ye M (2021) Local-global attentive adaptation for object detection. Eng Appl Artif Intell 100:104208","journal-title":"Eng Appl Artif Intell"},{"key":"2586_CR53","doi-asserted-by":"crossref","unstructured":"Cai Q, Pan Y, Ngo C-W, Tian X, Duan L, Yao T (2019) Exploring object relation in mean teacher for cross-domain detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11457\u201311466","DOI":"10.1109\/CVPR.2019.01172"},{"key":"2586_CR54","doi-asserted-by":"publisher","unstructured":"Deng J, Li W, Chen Y, Duan L (2021) Unbiased mean teacher for cross-domain object detection. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4089\u20134099 . https:\/\/doi.org\/10.1109\/CVPR46437.2021.00408","DOI":"10.1109\/CVPR46437.2021.00408"},{"key":"2586_CR55","doi-asserted-by":"crossref","unstructured":"Li S, Ye M, Zhu X, Zhou L, Xiong L (2022) Source-free object detection by learning to overlook domain style. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8014\u20138023","DOI":"10.1109\/CVPR52688.2022.00785"},{"key":"2586_CR56","doi-asserted-by":"crossref","unstructured":"Kennerley M, Wang J-G, Veeravalli B, Tan RT (2023) 2pcnet: Two-phase consistency training for day-to-night unsupervised domain adaptive object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11484\u201311493","DOI":"10.1109\/CVPR52729.2023.01105"},{"issue":"11","key":"2586_CR57","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: The kitti dataset. The International Journal of Robotics Research 32(11):1231\u20131237","journal-title":"The International Journal of Robotics Research"},{"issue":"9","key":"2586_CR58","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","volume":"126","author":"C Sakaridis","year":"2018","unstructured":"Sakaridis C, Dai D, Van Gool L (2018) Semantic foggy scene understanding with synthetic data. Int J Comput Vision 126(9):973\u2013992","journal-title":"Int J Comput Vision"},{"key":"2586_CR59","doi-asserted-by":"crossref","unstructured":"Johnson-Roberson M, Barto C, Mehta R, Sridhar SN, Rosaen K, Vasudevan R (2016) Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks? arXiv preprint arXiv:1610.01983","DOI":"10.1109\/ICRA.2017.7989092"},{"key":"2586_CR60","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"key":"2586_CR61","doi-asserted-by":"crossref","unstructured":"Sindagi VA, Oza P, Yasarla R, Patel VM (2020) Prior-based domain adaptive object detection for hazy and rainy conditions. In: European Conference on Computer Vision, pp. 763\u2013780. Springer","DOI":"10.1007\/978-3-030-58568-6_45"},{"key":"2586_CR62","doi-asserted-by":"publisher","unstructured":"Xu M, Wang H, Ni B, Tian Q, Zhang W (2020) Cross-domain detection via graph-induced prototype alignment. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12352\u201312361 . https:\/\/doi.org\/10.1109\/CVPR42600.2020.01237","DOI":"10.1109\/CVPR42600.2020.01237"},{"key":"2586_CR63","doi-asserted-by":"crossref","unstructured":"Lin C, Yuan Z, Zhao S, Sun P, Wang C, Cai J (2021) Domain-invariant disentangled network for generalizable object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8771\u20138780","DOI":"10.1109\/ICCV48922.2021.00865"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02586-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02586-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02586-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T10:59:40Z","timestamp":1757156380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02586-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,3]]},"references-count":63,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2586"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02586-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,3]]},"assertion":[{"value":"10 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}