{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T03:08:05Z","timestamp":1783480085273,"version":"3.55.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s44443-025-00329-3","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T13:49:21Z","timestamp":1762264161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Semi-supervised contrastive clustering with strong-weak augmentation for novel class discovery in open-world object detection"],"prefix":"10.1007","volume":"37","author":[{"given":"Fei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"329_CR1","doi-asserted-by":"crossref","unstructured":"Bendale A, Boult TE (2016) Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1563\u20131572","DOI":"10.1109\/CVPR.2016.173"},{"key":"329_CR2","doi-asserted-by":"crossref","unstructured":"Bosisio A, Berizzi A, Morotti A, Greco B, Iannarelli G, Moscatiello C, Boccaletti C, Noriega H (2021) Performance assessment of load profiles clustering methods based on silhouette analysis. In: 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC\/I&CPS Europe), pp. 1\u20136. IEEE","DOI":"10.1109\/EEEIC\/ICPSEurope51590.2021.9584629"},{"key":"329_CR3","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229. Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"329_CR4","doi-asserted-by":"crossref","unstructured":"Chang J, Wang L, Meng G, Xiang S, Pan C (2017) Deep adaptive image clustering. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5879\u20135887","DOI":"10.1109\/ICCV.2017.626"},{"key":"329_CR5","doi-asserted-by":"crossref","unstructured":"Chen X (2024) Vehicle object detection algorithm based on improved yolov8. In: 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL), pp. 1513\u20131516. IEEE","DOI":"10.1109\/CVIDL62147.2024.10603727"},{"issue":"4","key":"329_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3321386","volume":"66","author":"V Cohen-Addad","year":"2019","unstructured":"Cohen-Addad V, Kanade V, Mallmann-Trenn F, Mathieu C (2019) Hierarchical clustering: objective functions and algorithms. Journal of the ACM (JACM) 66(4):1\u201342","journal-title":"Journal of the ACM (JACM)"},{"key":"329_CR7","doi-asserted-by":"crossref","unstructured":"Deng D (2020) Dbscan clustering algorithm based on density. In: 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), pp. 949\u2013953. IEEE","DOI":"10.1109\/IFEEA51475.2020.00199"},{"key":"329_CR8","doi-asserted-by":"publisher","unstructured":"Dhamija AR, G\u00fcnther M, Ventura J, Boult TE (2020) The overlooked elephant of object detection: Open set. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1010\u20131019. https:\/\/doi.org\/10.1109\/WACV45572.2020.9093355","DOI":"10.1109\/WACV45572.2020.9093355"},{"key":"329_CR9","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vision 88:303\u2013338","journal-title":"Int J Comput Vision"},{"key":"329_CR10","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR"},{"key":"329_CR11","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: Exceeding yolo series in 2021. arXiv:2107.08430"},{"key":"329_CR12","doi-asserted-by":"crossref","unstructured":"Han J, Ren Y, Ding J, Pan X, Yan K, Xia G-S (2022) Expanding low-density latent regions for open-set object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9591\u20139600","DOI":"10.1109\/CVPR52688.2022.00937"},{"key":"329_CR13","doi-asserted-by":"crossref","unstructured":"Han K, Vedaldi A, Zisserman A (2019) Learning to discover novel visual categories via deep transfer clustering. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8401\u20138409","DOI":"10.1109\/ICCV.2019.00849"},{"key":"329_CR14","first-page":"4","volume":"2","author":"Z Huang","year":"2019","unstructured":"Huang Z, Zhou JT, Peng X, Zhang C, Zhu H, Lv J (2019) Multi-view spectral clustering network In IJCAI 2:4","journal-title":"Multi-view spectral clustering network In IJCAI"},{"issue":"10","key":"329_CR15","doi-asserted-by":"publisher","first-page":"9472","DOI":"10.1109\/TCSVT.2024.3399596","volume":"34","author":"D Huang","year":"2024","unstructured":"Huang D, Deng X, Chen D-H, Wen Z, Sun W, Wang C-D, Lai J-H (2024) Deep clustering with hybrid-grained contrastive and discriminative learning. IEEE Trans Circuits Syst Video Technol 34(10):9472\u20139483","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"329_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2025.105508","volume":"157","author":"Y Huang","year":"2025","unstructured":"Huang Y, Xi X, Luo R (2025) Ddmcb: open-world object detection empowered by denoising diffusion models and calibration balance. Image Vis Comput 157:105508. https:\/\/doi.org\/10.1016\/j.imavis.2025.105508","journal-title":"Image Vis Comput"},{"key":"329_CR17","doi-asserted-by":"crossref","unstructured":"Joseph K, Khan S, Khan FS, Balasubramanian VN (2021) Towards open world object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5830\u20135840","DOI":"10.1109\/CVPR46437.2021.00577"},{"issue":"13","key":"329_CR18","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci 114(13):3521\u20133526","journal-title":"Proc Natl Acad Sci"},{"key":"329_CR19","unstructured":"Knoblauch J, Husain H, Diethe T (2020) Optimal continual learning has perfect memory and is np-hard. In: International Conference on Machine Learning, pp. 5327\u20135337. PMLR"},{"issue":"12","key":"329_CR20","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li Z, Hoiem D (2017) Learning without forgetting. IEEE Trans Pattern Anal Mach Intell 40(12):2935\u20132947","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"329_CR21","doi-asserted-by":"publisher","first-page":"8547","DOI":"10.1609\/aaai.v35i10.17037","volume":"35","author":"Y Li","year":"2021","unstructured":"Li Y, Hu P, Liu Z, Peng D, Zhou JT, Peng X (2021) Contrastive clustering. Proceedings of the AAAI Conference on Artificial Intelligence 35:8547\u20138555","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"329_CR22","doi-asserted-by":"crossref","unstructured":"Li W, Guo X, Yuan Y (2023) Novel scenes & classes: Towards adaptive open-set object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15780\u201315790","DOI":"10.1109\/ICCV51070.2023.01446"},{"key":"329_CR23","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: Computer vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740\u2013755. Springer","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"329_CR24","doi-asserted-by":"crossref","unstructured":"Liu Y-C, Ma C-Y, Dai X, Tian J, Vajda P, He Z, Kira Z (2022) Open-set semi-supervised object detection. In: European Conference on Computer Vision, pp. 143\u2013159. Springer","DOI":"10.1007\/978-3-031-20056-4_9"},{"key":"329_CR25","unstructured":"Lopez-Paz D, Ranzato M (2017) Gradient episodic memory for continual learning. Advances in Neural Information Processing Systems 30"},{"key":"329_CR26","doi-asserted-by":"publisher","unstructured":"Mei X, Zhang K, Huang C, Chen X, Li M, Li Z, Ding W, Wu X (2024) Sodsr: a three-stage small object detection via super-resolution using optimizing combination. IEEE Transactions on Emerging Topics in Computational Intelligence 1\u201317. https:\/\/doi.org\/10.1109\/TETCI.2024.3452749","DOI":"10.1109\/TETCI.2024.3452749"},{"key":"329_CR27","doi-asserted-by":"crossref","unstructured":"Miller D, Dayoub F, Milford M, S\u00fcnderhauf N (2019) Evaluating merging strategies for sampling-based uncertainty techniques in object detection. In: 2019 International Conference on Robotics and Automation (icra), pp. 2348\u20132354. IEEE","DOI":"10.1109\/ICRA.2019.8793821"},{"issue":"5","key":"329_CR28","doi-asserted-by":"publisher","first-page":"4305","DOI":"10.1609\/aaai.v38i5.28227","volume":"38","author":"SS Mullappilly","year":"2024","unstructured":"Mullappilly SS, Gehlot AS, Anwer RM, Shahbaz Khan F, Cholakkal H (2024) Semi-supervised open-world object detection. Proceedings of the AAAI Conference on Artificial Intelligence 38(5):4305\u20134314. https:\/\/doi.org\/10.1609\/aaai.v38i5.28227","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"329_CR29","doi-asserted-by":"crossref","unstructured":"Prabhu A, Torr PH, Dokania PK (2020) Gdumb: A simple approach that questions our progress in continual learning. In: European Conference on Computer Vision, pp. 524\u2013540. Springer","DOI":"10.1007\/978-3-030-58536-5_31"},{"issue":"6","key":"329_CR30","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"329_CR31","doi-asserted-by":"crossref","unstructured":"Sarfraz S, Sharma V, Stiefelhagen R (2019) Efficient parameter-free clustering using first neighbor relations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8934\u20138943","DOI":"10.1109\/CVPR.2019.00914"},{"key":"329_CR32","first-page":"80716","volume":"8","author":"KP Sinaga","year":"2020","unstructured":"Sinaga KP, Yang M-S (2020) Unsupervised k-means clustering algorithm IEEE access 8:80716\u201380727","journal-title":"Unsupervised k-means clustering algorithm IEEE access"},{"key":"329_CR33","unstructured":"Tian Y, Ye Q, Doermann D (2025) Yolov12: Attention-centric real-time object detectors. arXiv:2502.12524"},{"key":"329_CR34","doi-asserted-by":"crossref","unstructured":"Van\u00a0Gansbeke W, Vandenhende S, Georgoulis S, Proesmans M, Van\u00a0Gool L (2020) Scan: Learning to classify images without labels. In: European Conference on Computer Vision, pp. 268\u2013285. Springer","DOI":"10.1007\/978-3-030-58607-2_16"},{"issue":"3","key":"329_CR35","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1109\/TETCI.2023.3235381","volume":"7","author":"H Wang","year":"2023","unstructured":"Wang H, Xu Y, Wang Z, Cai Y, Chen L, Li Y (2023) Centernet-auto: a multi-object visual detection algorithm for autonomous driving scenes based on improved centernet. IEEE Transactions on Emerging Topics in Computational Intelligence 7(3):742\u2013752. https:\/\/doi.org\/10.1109\/TETCI.2023.3235381","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"329_CR36","doi-asserted-by":"crossref","unstructured":"Wang Z, Li Y, Chen X, Lim S-N, Torralba A, Zhao H, Wang S (2023) Detecting everything in the open world: Towards universal object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11433\u201311443","DOI":"10.1109\/CVPR52729.2023.01100"},{"key":"329_CR37","unstructured":"Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478\u2013487 . PMLR"},{"key":"329_CR38","doi-asserted-by":"crossref","unstructured":"Yin H, Chen L (2024) Enhanced road vehicle object detection based on improved deformable detr. In: 2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), pp. 2227\u20132230 . IEEE","DOI":"10.1109\/AINIT61980.2024.10581739"},{"issue":"3","key":"329_CR39","doi-asserted-by":"publisher","first-page":"2467","DOI":"10.1109\/TETCI.2024.3367821","volume":"8","author":"X Yue","year":"2024","unstructured":"Yue X, Meng L (2024) Yolo-sm: a lightweight single-class multi-deformation object detection network. IEEE Transactions on Emerging Topics in Computational Intelligence 8(3):2467\u20132480. https:\/\/doi.org\/10.1109\/TETCI.2024.3367821","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"329_CR40","doi-asserted-by":"crossref","unstructured":"Zhang Y, Guo Z, Wu J, Tian Y, Tang H, Guo X (2022) Real-time vehicle detection based on improved yolo v5. Sustainability 14(19):12274","DOI":"10.3390\/su141912274"},{"issue":"5","key":"329_CR41","doi-asserted-by":"publisher","first-page":"3496","DOI":"10.1109\/TCSVT.2023.3326279","volume":"34","author":"X Zhao","year":"2023","unstructured":"Zhao X, Ma Y, Wang D, Shen Y, Qiao Y, Liu X (2023) Revisiting open world object detection. IEEE Trans Circuits Syst Video Technol 34(5):3496\u20133509","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"5","key":"329_CR42","doi-asserted-by":"publisher","first-page":"3496","DOI":"10.1109\/TCSVT.2023.3326279","volume":"34","author":"X Zhao","year":"2024","unstructured":"Zhao X, Ma Y, Wang D, Shen Y, Qiao Y, Liu X (2024) Revisiting open world object detection. IEEE Trans Circuits Syst Video Technol 34(5):3496\u20133509. https:\/\/doi.org\/10.1109\/TCSVT.2023.3326279","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"329_CR43","doi-asserted-by":"crossref","unstructured":"Zheng J, Li W, Hong J, Petersson L, Barnes N (2022) Towards open-set object detection and discovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3961\u20133970","DOI":"10.1109\/CVPRW56347.2022.00441"},{"key":"329_CR44","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2010) Deformable detr: Deformable transformers for end-to-end object detection. arxiv 2020. arXiv:2010.04159. 3"},{"key":"329_CR45","unstructured":"Zohar O, Lozano A, Goel S, Yeung S, Wang K-C (2023) Open world object detection in the era of foundation models. arXiv preprint arXiv:2312.05745"},{"key":"329_CR46","doi-asserted-by":"crossref","unstructured":"Zohar O, Wang K-C, Yeung S (2023) Prob: Probabilistic objectness for open world object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11444\u201311453","DOI":"10.1109\/CVPR52729.2023.01101"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00329-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00329-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00329-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:46:15Z","timestamp":1763567175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00329-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":46,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["329"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00329-3","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"6 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 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":"Data will be made available on request.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and material"}},{"value":"The authors declare no known competing financial interests or personal relationships that could have influenced the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"296"}}