{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:35:04Z","timestamp":1760060104613,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Inspired by the human ability to learn continuously and adapt to changing environments, researchers have proposed Online Open-World Object Detection (OLOWOD). This emerging paradigm faces the challenges of detecting known categories, discovering unknown ones, continuously learning new categories, and mitigating catastrophic forgetting. To address these challenges, we propose Category Prototype-guided Streaming Multi-Level Perturbation, PSMP, a plug-and-play method for OLOWOD. PSMP, comprising semantic-level, enhanced data-level, and enhanced feature-level perturbations jointly guided by category prototypes, operates at different representational levels to collaboratively extract latent knowledge across tasks and improve adaptability. In addition, PSMP constructs the \u201ccontrastive tension\u201d based on the relationships among category prototypes. This mechanism inherently leverages the symmetric structure formed by class prototypes in the latent space, where prototypes of semantically similar categories tend to align symmetrically or equidistantly. By guiding perturbations along these symmetric axes, the model can achieve more balanced generalization between known and unknown categories. PSMP requires no additional annotations, is lightweight in design, and can be seamlessly integrated into existing OWOD methods. Extensive experiments show that PSMP achieves an improvement of approximately 1.5% to 3% in mAP for known categories compared to conventional online training methods while significantly increasing the Unknown Recall (UR) by around 4.6%.<\/jats:p>","DOI":"10.3390\/sym17081237","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T07:49:58Z","timestamp":1754380198000},"page":"1237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PSMP: Category Prototype-Guided Streaming Multi-Level Perturbation for Online Open-World Object Detection"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1172-3642","authenticated-orcid":false,"given":"Shibo","family":"Gu","sequence":"first","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Shandong Province Higher Education Institutions Future Industry Engineering Research Center for Artificial Intelligence Safety, Qingdao 266580, China"},{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Sun","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Shandong Province Higher Education Institutions Future Industry Engineering Research Center for Artificial Intelligence Safety, Qingdao 266580, China"},{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhao","family":"Bai","sequence":"additional","affiliation":[{"name":"China Electronics Technology Group Corporation\u2019s 22nd Research Institute (Qingdao), Qingdao 266107, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziliang","family":"Chen","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Shandong Province Higher Education Institutions Future Industry Engineering Research Center for Artificial Intelligence Safety, Qingdao 266580, China"},{"name":"Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,5]]},"reference":[{"key":"ref_1","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_2","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_3","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_4","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_9","first-page":"2","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Joseph, K., Khan, S., Khan, F.S., and Balasubramanian, V.N. (2021, January 20\u201325). Towards open world object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00577"},{"key":"ref_12","unstructured":"Gupta, A., Narayan, S., Joseph, K., Khan, S., Khan, F.S., and Shah, M. (2019, January 20). Ow-detr: Open-world detection transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3496","DOI":"10.1109\/TCSVT.2023.3326279","article-title":"Revisiting Open World Object Detection","volume":"34","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zohar, O., Wang, K.-C., and Yeung, S. (2023, January 24). PROB: Probabilistic Objectness for Open World Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01101"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, S., Wang, Y., Wei, Y., Fan, J., Li, T.H., Liu, H., and Lv, F. (2023, January 24). CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01885"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, J., Ma, L., Li, Z., Peng, Y., and Xie, S. (2022, January 16\u201319). Open-world object detection via discriminative class prototype learning. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9897461"},{"key":"ref_17","unstructured":"Pershouse, D., Dayoub, F., Miller, D., and S\u00fcnderhauf, N. (2023). Addressing the challenges of open-world object detection. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110472","DOI":"10.1016\/j.patcog.2024.110472","article-title":"BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection","volume":"152","author":"Chen","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Van der Groen, O., Potok, W., Wenderoth, N., Edwards, G., Mattingley, J.B., and Edwards, D. (2022). Using noise for the better: The effects of transcranial random noise stimulation on the brain and behavior. Neurosci. Biobehav. Rev., 138.","DOI":"10.1016\/j.neubiorev.2022.104702"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-based convolutional networks for accurate object detection and segmentation","volume":"38","author":"Girshick","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., and Wang, C. (2022, January 18\u201324). Sparse r-cnn: End-to-end object detection with learnable proposals. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_25","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., and Koltun, V. (2021, January 10\u201317). Vision transformers for dense prediction. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., and Feichtenhofer, C. (2021, January 10\u201317). Multiscale vision transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00675"},{"key":"ref_28","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 10\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_29","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-End Object Detection with Transformers. arXiv.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_30","unstructured":"Wu, Z., Lu, Y., Chen, X., Wu, Z., Kang, L., and Yu, J. UC-OWOD: Unknown-Classified Open World Object Detection, Springer Nature."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"154","DOI":"10.37256\/aie.4220233058","article-title":"A Framework for Open World Object Detection","volume":"4","author":"Shaheen","year":"2023","journal-title":"Artif. Intell. Evol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TCSVT.2024.3480691","article-title":"Open World Object Detection: A Survey","volume":"35","author":"Li","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mullappilly, S.S., Gehlot, A.S., Anwer, R.M., Khan, F.S., and Cholakkal, H. (2024, January 20\u201327). Semi-supervised Open-World Object Detection. Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI)\/36th Conference on Innovative Applications of Artificial Intelligence\/14th Symposium on Educational Advances in Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i5.28227"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1109\/TCSVT.2023.3322465","article-title":"Instance-Dictionary Learning for Open-World Object Detection in Autonomous Driving Scenarios","volume":"34","author":"Ma","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1109\/TIP.2024.3459589","article-title":"Recalling Unknowns Without Losing Precision: An Effective Solution to Large Model-Guided Open World Object Detection","volume":"34","author":"He","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s11227-024-06910-3","article-title":"Enhancing open-world object detection with AIGC-generated datasets and elastic weight consolidation","volume":"81","author":"Xue","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11340","DOI":"10.1109\/TNNLS.2025.3559940","article-title":"Unsupervised Recognition of Unknown Objects for Open-World Object Detection","volume":"36","author":"Fang","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, R., Wang, J., Chen, Y., Zheng, Z., Cui, K., and Su, J. (2024, January 18\u201320). Class-Agnostic Detection of Unknown Objects from Foreground Improves Robust Open World Object Detection. Proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, Urumqi, China.","DOI":"10.1007\/978-981-97-8858-3_6"},{"key":"ref_39","first-page":"694","article-title":"OW-Adapter: Human-Assisted Open-World Object Detection with a Few Examples","volume":"30","author":"Jamonnak","year":"2024","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Castro, F.M., Mar\u00edn-Jim\u00e9nez, M.J., Guil, N., Schmid, C., and Alahari, K. (2018, January 8\u201314). End-to-end incremental learning. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"109488","DOI":"10.1016\/j.patcog.2023.109488","article-title":"Class-incremental object detection","volume":"139","author":"Dong","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"109561","DOI":"10.1016\/j.patcog.2023.109561","article-title":"Exemplar-free class incremental learning via discriminative and comparable parallel one-class classifiers","volume":"140","author":"Sun","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_45","first-page":"27075","article-title":"Exploring example influence in continual learning","volume":"35","author":"Sun","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tiwari, R., Killamsetty, K., Iyer, R., and Shenoy, P. (2022, January 19\u201320). Gcr: Gradient coreset based replay buffer selection for continual learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00020"},{"key":"ref_47","unstructured":"Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., and Tesauro, G. (2018). Learning to learn without forgetting by maximizing transfer and minimizing interference. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"108907","DOI":"10.1016\/j.patcog.2022.108907","article-title":"Multi-criteria selection of rehearsal samples for continual learning","volume":"132","author":"Zhuang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C.H. (2016, January 27\u201330). icarl: Incremental classifier and representation learning. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref_50","unstructured":"Dong, J., Liang, W., Cong, Y., and Sun, G. (2024, January 16\u201322). Heterogeneous forgetting compensation for class-incremental learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seattle, WA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"108863","DOI":"10.1016\/j.patcog.2022.108863","article-title":"Multi-View correlation distillation for incremental object detection","volume":"131","author":"Yang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, X., Shang-Guan, Y., and Gupta, A. (2021, January 10\u201317). Wanderlust: Online Continual Object Detection in the Real World. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01065"},{"key":"ref_53","unstructured":"Li, M., Yan, Z., and Li, C. Class Incremental Learning with Important and Diverse Memory, Springer Nature."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Nokhwal, S., and Kumar, N. (2023, January 25\u201326). DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning. Proceedings of the 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI), Mexico City, Mexico.","DOI":"10.1109\/ISCMI59957.2023.10458500"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7711","DOI":"10.1109\/TNNLS.2024.3394511","article-title":"Feature Noise Boosts DNN Generalization Under Label Noise","volume":"36","author":"Zeng","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_56","unstructured":"Dhifallah, O., and Lu, Y. (2021, January 18\u201324). On the inherent regularization effects of noise injection during training. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"104778","DOI":"10.1016\/j.dsp.2024.104778","article-title":"Robustness enhancement in neural networks with alpha-stable training noise","volume":"156","author":"Yuan","year":"2025","journal-title":"Digit. Signal Process."},{"key":"ref_58","unstructured":"Kim, H.-E., Hwang, S., and Cho, K. (2016). Semantic Noise Modeling for Better Representation Learning. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Bansal, A., Sikka, K., Sharma, G., Chellappa, R., and Divakaran, A. (2018). Zero-Shot Object Detection. Computer Vision\u2014ECCV 2018, Springer Nature.","DOI":"10.1007\/978-3-030-01246-5_24"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Dhamija, A.R., G\u00fcnther, M., Ventura, J., and Boult, T.E. (2020, January 1\u20135). The Overlooked Elephant of Object Detection: Open Set. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093355"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Miller, D., Zhou, Z., Bambos, N., and Ben-Gal, I. (2018, January 20\u201324). Optimal Sensing for Patient Health Monitoring. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422884"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yue, Z., Hua, X.-S., and Zhang, H. (2023, January 1\u20136). Random boxes are open-world object detectors. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00573"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"9209","DOI":"10.1109\/TPAMI.2021.3124133","article-title":"Incremental Object Detection via Meta-Learning","volume":"44","author":"Joseph","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Shmelkov, K., Schmid, C., and Alahari, K. (2017, January 22\u201329). Incremental learning of object detectors without catastrophic forgetting. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.368"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.patrec.2020.09.030","article-title":"Faster ILOD: Incremental learning for object detectors based on faster RCNN","volume":"140","author":"Peng","year":"2020","journal-title":"Pattern Recognit. 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