{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:06:22Z","timestamp":1757617582167,"version":"3.44.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s13042-025-02566-4","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T16:00:57Z","timestamp":1740067257000},"page":"5181-5194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised cross-domain object detection based on dynamic smooth cross entropy"],"prefix":"10.1007","volume":"16","author":[{"given":"BoJun","family":"Xie","sequence":"first","affiliation":[]},{"given":"ZhiJin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"JunFen","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"key":"2566_CR1","doi-asserted-by":"crossref","unstructured":"Chen Y, Li W et\u00a0al (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":"2566_CR2","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn K, Berthelot D et al (2020) Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv Neural Inf Process Syst 33:596\u2013608","journal-title":"Adv Neural Inf Process Syst"},{"key":"2566_CR3","first-page":"18408","volume":"34","author":"B Zhang","year":"2021","unstructured":"Zhang B, Wang Y et al (2021) Flexmatch: boosting semi-supervised learning with curriculum pseudo labeling. Adv Neural Inf Process Syst 34:18408\u201318419","journal-title":"Adv Neural Inf Process Syst"},{"key":"2566_CR4","unstructured":"Jiang J, Chen B et\u00a0al (2022) Decoupled adaptation for cross-domain object detection. In: ICLR"},{"key":"2566_CR5","unstructured":"Wang Y, Chen H et\u00a0al (2023) Freematch: self-adaptive thresholding for semi-supervised learning. In: International conference on learning representations (ICLR), pp 1\u201320"},{"key":"2566_CR6","unstructured":"Rizve MN, Duarte K et\u00a0al (2021) In defense of pseudo-labeling: an uncertainty-aware pseudo-label selection framework for semi-supervised learning. In: International conference on learning representations (ICLR)"},{"key":"2566_CR7","unstructured":"Chen H, Tao R et\u00a0al (2023) Softmatch: addressing the quantity-quality trade-off in semi-supervised learning. In: International conference on learning representations (ICLR)"},{"key":"2566_CR8","doi-asserted-by":"publisher","first-page":"106442","DOI":"10.1016\/j.engappai.2023.106442","volume":"123","author":"D Wan","year":"2023","unstructured":"Wan D, Lu R et al (2023) Mixed local channel attention for object detection. Eng Appl Artif Intell 123:106442","journal-title":"Eng Appl Artif Intell"},{"key":"2566_CR9","doi-asserted-by":"publisher","first-page":"102709","DOI":"10.1016\/j.aei.2024.102709","volume":"62","author":"D Wan","year":"2024","unstructured":"Wan D, Lu R, Hu B et al (2024) Yolo-mif: improved yolov8 with multi-information fusion for object detection in gray-scale images. Adv Eng Inform 62:102709","journal-title":"Adv Eng Inform"},{"key":"2566_CR10","doi-asserted-by":"crossref","unstructured":"Li Y-J, Dai X et\u00a0al (2022) Cross-domain adaptive teacher for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7581\u20137590","DOI":"10.1109\/CVPR52688.2022.00743"},{"key":"2566_CR11","doi-asserted-by":"crossref","unstructured":"Kennerley M, Wang J-G et\u00a0al (2024) Cat: exploiting inter-class dynamics for domain adaptive object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 16541\u201316550","DOI":"10.1109\/CVPR52733.2024.01565"},{"key":"2566_CR12","unstructured":"Ganin Y , Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning (ICML). PMLR, pp 1180\u20131189"},{"key":"2566_CR13","unstructured":"Long M, Zhu H et\u00a0al (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning (ICML), pp 2208\u20132217"},{"key":"2566_CR14","unstructured":"Long M, Cao Z et\u00a0al (2018) Conditional adversarial domain adaptation. In: Adv Neural Inf Process Syst 31"},{"key":"2566_CR15","unstructured":"Zhang Y, Wang X et\u00a0al (2023) Free lunch for domain adversarial training: environment label smoothing. arXiv preprint arXiv:2302.00194"},{"key":"2566_CR16","doi-asserted-by":"crossref","unstructured":"Sun T, Lu C et\u00a0al (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":"2566_CR17","doi-asserted-by":"publisher","first-page":"120355","DOI":"10.1016\/j.eswa.2023.120355","volume":"228","author":"D Wan","year":"2023","unstructured":"Wan D, Lu R et al (2023) Random interpolation resize: a free image data augmentation method for object detection in industry. Expert Syst Appl 228:120355","journal-title":"Expert Syst Appl"},{"key":"2566_CR18","doi-asserted-by":"crossref","unstructured":"Xu M, Qin L et\u00a0al (2023) Multi-view adversarial discriminator: mine the non-causal factors for object detection in unseen domains. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8103\u20138112","DOI":"10.1109\/CVPR52729.2023.00783"},{"key":"2566_CR19","doi-asserted-by":"crossref","unstructured":"Deng J, Li W et\u00a0al (2021) Unbiased mean teacher for cross-domain object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4091\u20134101","DOI":"10.1109\/CVPR46437.2021.00408"},{"key":"2566_CR20","doi-asserted-by":"crossref","unstructured":"Deng J, Xu D et\u00a0al (2023) Harmonious teacher for cross-domain object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 23829\u201323838","DOI":"10.1109\/CVPR52729.2023.02282"},{"key":"2566_CR21","doi-asserted-by":"crossref","unstructured":"Li S, Zhang J et\u00a0al (2021) Dynamic domain adaptation for efficient inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7832\u20137841","DOI":"10.1109\/CVPR46437.2021.00774"},{"key":"2566_CR22","doi-asserted-by":"publisher","first-page":"126624","DOI":"10.1016\/j.neucom.2023.126624","volume":"555","author":"Z Zheng","year":"2023","unstructured":"Zheng Z, Teng S et al (2023) Selected confidence sample labeling for domain adaptation. Neurocomputing 555:126624","journal-title":"Neurocomputing"},{"key":"2566_CR23","doi-asserted-by":"crossref","unstructured":"Karim N, Mithun NC et\u00a0al (2023) C-sfda: a curriculum learning aided self-training framework for efficient source free domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 24120\u201324131","DOI":"10.1109\/CVPR52729.2023.02310"},{"key":"2566_CR24","doi-asserted-by":"crossref","unstructured":"Cao S, Joshi D et\u00a0al (2023) Contrastive mean teacher for domain adaptive object detectors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 23839\u201323848","DOI":"10.1109\/CVPR52729.2023.02283"},{"key":"2566_CR25","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv Neural Inf Process Syst 30"},{"key":"2566_CR26","doi-asserted-by":"crossref","unstructured":"Saito K, Ushiku Y et\u00a0al (2019) Strong-weak distribution alignment for adaptive object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6956\u20136965","DOI":"10.1109\/CVPR.2019.00712"},{"key":"2566_CR27","doi-asserted-by":"crossref","unstructured":"Kim S, Choi J et\u00a0al (2019) Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. In: Proceedings of the IEEE international conference on computer vision, pp 6092\u20136101","DOI":"10.1109\/ICCV.2019.00619"},{"key":"2566_CR28","doi-asserted-by":"crossref","unstructured":"He Z, Zhang L (2019) Multi-adversarial faster-rcnn for unrestricted object detection. In: Proceedings of the IEEE international conference on computer vision, pp 6668\u20136677","DOI":"10.1109\/ICCV.2019.00677"},{"key":"2566_CR29","unstructured":"Shen Z, Maheshwari H et\u00a0al (2019) Scl: towards accurate domain adaptive object detection via gradient detach based stacked complementary losses. arXiv preprint arXiv:1911.02559"},{"key":"2566_CR30","doi-asserted-by":"crossref","unstructured":"Xu C-D, Zhao X-R et\u00a0al (2020) Exploring categorical regularization for domain adaptive object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 11724\u201311733","DOI":"10.1109\/CVPR42600.2020.01174"},{"key":"2566_CR31","doi-asserted-by":"crossref","unstructured":"Zhao L, Wang L (2022) Task-specific inconsistency alignment for domain adaptive object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 14217\u201314226","DOI":"10.1109\/CVPR52688.2022.01382"},{"key":"2566_CR32","doi-asserted-by":"crossref","unstructured":"Zhao Z, Guo Y (2020) Adaptive object detection with dual multi-label prediction. In: Computer vision-ECCV, et al (2020) 16th European conference, Glasgow, UK, August 23\u201328, 2020. Proceedings, part XXVIII, vol 16, pp 54\u201369","DOI":"10.1007\/978-3-030-58604-1_4"},{"key":"2566_CR33","unstructured":"Chen M, Chen W et\u00a0al (2022) Learning domain adaptive object detection with probabilistic teacher. arXiv preprint arXiv:2206.06293"},{"key":"2566_CR34","doi-asserted-by":"crossref","unstructured":"Inoue N, Furuta R et\u00a0al (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":"2566_CR35","doi-asserted-by":"crossref","unstructured":"Vs V, Gupta V et\u00a0al (2021) Mega-cda: memory guided attention for category-aware unsupervised domain adaptive object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4516\u20134526","DOI":"10.1109\/CVPR46437.2021.00449"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02566-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02566-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02566-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:13:43Z","timestamp":1757139223000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02566-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,20]]},"references-count":35,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2566"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02566-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,2,20]]},"assertion":[{"value":"23 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2025","order":3,"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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}