{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:27:26Z","timestamp":1782401246096,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071340"],"award-info":[{"award-number":["42071340"]}],"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":["2211000211000-01"],"award-info":[{"award-number":["2211000211000-01"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Song Shan Laboratory","award":["42071340"],"award-info":[{"award-number":["42071340"]}]},{"name":"Song Shan Laboratory","award":["2211000211000-01"],"award-info":[{"award-number":["2211000211000-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate detection of relevant vehicles, pedestrians, and other targets on the road plays a crucial role in ensuring the safety of autonomous driving. In recent years, object detectors based on Transformers or CNNs have achieved excellent performance in the fully supervised paradigm. However, when the trained model is directly applied to unfamiliar scenes where the training data and testing data have different distributions statistically, the model\u2019s performance may decrease dramatically. To address this issue, unsupervised domain adaptive object detection methods have been proposed. However, these methods often exhibit decreasing performance when the gap between the source and target domains increases. Previous works mainly focused on utilizing the style gap to reduce the domain gap while ignoring the content gap. To tackle this challenge, we introduce a novel method called IDI-SCD that effectively addresses both the style and content gaps simultaneously. Firstly, the domain gap is reduced by disentangling it into the style gap and content gap, generating corresponding intermediate domains in the meanwhile. Secondly, during training, we focus on one single domain gap at a time to achieve inter-domain invariance. That is, the content gap is tackled while maintaining the style gap, and vice versa. In addition, the style-invariant loss is used to narrow down the style gap, and the mean teacher self-training framework is used to narrow down the content gap. Finally, we introduce a multiscale fusion strategy to enhance the quality of pseudo-labels, which mainly focus on enhancing the detection performance for extreme-scale objects (very large or very small objects). We conduct extensive experiments on four mainstream datasets of in-vehicle images. The experimental results demonstrate the effectiveness of our method and its superiority over most of the existing methods.<\/jats:p>","DOI":"10.3390\/rs16020304","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T08:27:07Z","timestamp":1704961627000},"page":"304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Inter-Domain Invariant Cross-Domain Object Detection Using Style and Content Disentanglement for In-Vehicle Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8459-080X","authenticated-orcid":false,"given":"Zhipeng","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-5417","authenticated-orcid":false,"given":"Ziquan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2405-2038","authenticated-orcid":false,"given":"Zhenchao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbin","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2584","DOI":"10.1109\/TITS.2017.2658662","article-title":"Overview of environment perception for intelligent vehicles","volume":"18","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gopalan, R., Li, R., and Chellappa, R. (2011, January 6\u201313). Domain adaptation for object recognition: An unsupervised approach. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Chen, X., and Gool, L.V. (2019, January 15\u201320). Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00194"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., and Van Gool, L. (2018, January 18\u201323). Domain adaptive faster r-cnn for object detection in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00352"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400066","article-title":"A survey of unsupervised deep domain adaptation","volume":"11","author":"Wilson","year":"2020","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, X., Yuan, L., and Feng, J. (2019, January 15\u201320). Few-shot adaptive faster r-cnn. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00734"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhuang, C., Han, X., Huang, W., and Scott, M. (2020, January 7\u201312). ifan: Image-instance full alignment networks for adaptive object detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.7015"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rezaeianaran, F., Shetty, R., Aljundi, R., Reino, D.O., Zhang, S., and Schiele, B. (2021, January 11\u201317). Seeking similarities over differences: Similarity-based domain alignment for adaptive object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00907"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Saito, K., Ushiku, Y., Harada, T., and Saenko, K. (2019, January 15\u201320). Strong-weak distribution alignment for adaptive object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00712"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xie, R., Yu, F., Wang, J., Wang, Y., and Zhang, L. (2019, January 27\u201328). Multi-level domain adaptive learning for cross-domain detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00401"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, H., Tian, Y., Wang, K., He, H., and Wang, F.Y. (2019, January 14\u201319). Synthetic-to-real domain adaptation for object instance segmentation. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851791"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, C., Zheng, Z., Ding, X., Huang, Y., and Dou, Q. (2020, January 13\u201319). Harmonizing transferability and discriminability for adapting object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00889"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhu, X., Pang, J., Yang, C., Shi, J., and Lin, D. (2019, January 15\u201320). Adapting object detectors via selective cross-domain alignment. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00078"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, W., Cao, Y., Zhang, J., He, F., Zha, Z.J., Wen, Y., and Tao, D. (2021, January 20\u201324). Exploring sequence feature alignment for domain adaptive detection transformers. Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China.","DOI":"10.1145\/3474085.3475317"},{"key":"ref_15","first-page":"603","article-title":"Cross-domain Adaptive Object Detection Based on Refined Knowledge Transfer and Mined Guidance in Autonomous Vehicles","volume":"7","author":"Wang","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cai, Q., Pan, Y., Ngo, C.W., Tian, X., Duan, L., and Yao, T. (2019, January 15\u201320). Exploring object relation in mean teacher for cross-domain detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01172"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Deng, J., Li, W., Chen, Y., and Duan, L. (2021, January 11\u201317). Unbiased mean teacher for cross-domain object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Montreal, QC, Canada.","DOI":"10.1109\/CVPR46437.2021.00408"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, Y.J., Dai, X., Ma, C.Y., Liu, Y.C., Chen, K., Wu, B., He, Z., Kitani, K., and Vajda, P. (2022, January 18\u201324). Cross-domain adaptive teacher for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00743"},{"key":"ref_19","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hsu, H.K., Yao, C.H., Tsai, Y.H., Hung, W.C., Tseng, H.Y., Singh, M., and Yang, M.H. (2020, January 1\u20135). Progressive domain adaptation for object detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093358"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Arruda, V.F., Paixao, T.M., Berriel, R.F., De Souza, A.F., Badue, C., Sebe, N., and Oliveira-Santos, T. (2019, January 14\u201319). Cross-domain car detection using unsupervised image-to-image translation: From day to night. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852008"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Oza, P., Sindagi, V.A., Sharmini, V.V., and Patel, V.M. (2023). Unsupervised domain adaptation of object detectors: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201324.","DOI":"10.1109\/TPAMI.2022.3217046"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_24","unstructured":"Tarvainen, A., and Valpola, H. (2017, January 4\u20139). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_25","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, Kauai, HI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust real-time face detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_31","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_33","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_35","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_36","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_37","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Lecture Notes in Computer Science, Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer. Part I 14."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_39","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_40","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). Lecture Notes in Computer Science, Proceedings of the Computer Vision\u2014ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer. Part I 16."},{"key":"ref_41","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 11\u201317). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_44","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Lecture Notes in Computer Science, Proceedings of the Computer Vision\u2014ECCV 2014: 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014, Springer. Part V 13."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cao, S., Joshi, D., Gui, L.Y., and Wang, Y.X. (2023, January 18\u201322). Contrastive Mean Teacher for Domain Adaptive Object Detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02283"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gong, K., Li, S., Li, S., Zhang, R., Liu, C.H., and Chen, Q. (2022, January 10\u201313). Improving Transferability for Domain Adaptive Detection Transformers. Proceedings of the 30th ACM International Conference on Multimedia, Lisbon, Portugal.","DOI":"10.1145\/3503161.3548246"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"105774","DOI":"10.1016\/j.engappai.2022.105774","article-title":"Progressive cross-domain knowledge distillation for efficient unsupervised domain adaptive object detection","volume":"119","author":"Li","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, D., Chen, Y., Karianakis, N., Shen, T., Yu, P., Lymberopoulos, D., Lu, S., Shi, W., and Chen, X. (2022, January 3\u20138). Sc-uda: Style and content gaps aware unsupervised domain adaptation for object detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00113"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhang, B., Zhang, J., Zhang, W., Gan, Z., Tian, G., Zhu, W., Wang, Y., and Wang, C. (2023, January 18\u201322). MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00712"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mattolin, G., Zanella, L., Ricci, E., and Wang, Y. (2023, January 18\u201322). ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing. Proceedings of theIEEE\/CVF Winter Conference on Applications of Computer Vision, Vancouver, BC, Canada.","DOI":"10.1109\/WACV56688.2023.00050"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S.N., Rosaen, K., and Vasudevan, R. (2016). Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?. arXiv.","DOI":"10.1109\/ICRA.2017.7989092"},{"key":"ref_52","unstructured":"Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., and Darrell, T. (2018). Bdd100k: A diverse driving video database with scalable annotation tooling. arXiv."},{"key":"ref_53","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (July, January 26). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_55","unstructured":"Bottou, L. (2012). Neural Networks: Tricks of the Trade, Springer. [2nd ed.]."},{"key":"ref_56","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv."},{"key":"ref_57","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). Pytorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yoo, J., Chung, I., and Kwak, N. (2022, January 23\u201327). Unsupervised domain adaptation for one-stage object detector using offsets to bounding box. Proceedings of the European Conference on Computer Vision, ECCV 2022: Computer Vision\u2014ECCV 2022, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19827-4_40"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Xu, C.D., Zhao, X.R., Jin, X., and Wei, X.S. (2020, January 13\u201319). Exploring categorical regularization for domain adaptive object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01174"},{"key":"ref_60","first-page":"8954","article-title":"Self-adversarial disentangling for specific domain adaptation","volume":"45","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_61","unstructured":"Jiang, J., Chen, B., Wang, J., and Long, M. (2021). Decoupled adaptation for cross-domain object detection. arXiv."},{"key":"ref_62","unstructured":"Hsu, C.C., Tsai, Y.H., Lin, Y.Y., and Yang, M.H. (2020). Lecture Notes in Computer Science, Proceedings of the Computer Vision\u2014ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer. Part IX 16."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, X., and Yuan, Y. (2022, January 18\u201324). Sigma: Semantic-complete graph matching for domain adaptive object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00522"},{"key":"ref_64","unstructured":"Zhang, L., Zhou, W., Fan, H., Luo, T., and Ling, H. (2023). Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1109\/TIP.2023.3255106","article-title":"Integrated Multiscale Domain Adaptive YOLO","volume":"32","author":"Hnewa","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/TIV.2022.3165353","article-title":"Cross-domain object detection for autonomous driving: A stepwise domain adaptative YOLO approach","volume":"7","author":"Li","year":"2022","journal-title":"IEEE Trans. Intell. 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