{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:27:41Z","timestamp":1771025261870,"version":"3.50.1"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2023J011437"],"award-info":[{"award-number":["2023J011437"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2023J011437"],"award-info":[{"award-number":["2023J011437"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2023J011437"],"award-info":[{"award-number":["2023J011437"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2023J011437"],"award-info":[{"award-number":["2023J011437"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00371-025-03869-x","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T23:03:52Z","timestamp":1743548632000},"page":"8285-8301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improved diffusion object detection through deformable sigmoid variance and adjustable sampling strategy"],"prefix":"10.1007","volume":"41","author":[{"given":"Teng","family":"Fei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianle","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wuzhi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huankang","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guowei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"key":"3869_CR1","doi-asserted-by":"crossref","unstructured":"Huang, Y., Huang, J., Liu, J., Yan, M., Dong, Y., Lyu, J., Chen, C., Chen, S.: Wavedm: Wavelet-based diffusion models for image restoration. IEEE Transactions on Multimedia (2024)","DOI":"10.1109\/TMM.2024.3359769"},{"issue":"4","key":"3869_CR2","first-page":"4713","volume":"45","author":"C Saharia","year":"2022","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713\u20134726 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3869_CR3","doi-asserted-by":"crossref","unstructured":"Qiu, X., Han, C., Zhang, Z., Li, B., Guo, T., Nie, X.: Diffbfr: Bootstrapping diffusion model towards blind face restoration. arXiv preprint arXiv:2305.04517 (2023)","DOI":"10.1145\/3581783.3611731"},{"key":"3869_CR4","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhao, Y., Xiao, Z., Hou, T.: Ufogen: You forward once large scale text-to-image generation via diffusion gans. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8196\u20138206 (2024)","DOI":"10.1109\/CVPR52733.2024.00783"},{"key":"3869_CR5","doi-asserted-by":"crossref","unstructured":"Liu, X., Park, D.H., Azadi, S., Zhang, G., Chopikyan, A., Hu, Y., Shi, H., Rohrbach, A., Darrell, T.: More control for free! image synthesis with semantic diffusion guidance. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 289\u2013299 (2023)","DOI":"10.1109\/WACV56688.2023.00037"},{"key":"3869_CR6","unstructured":"Ye, F., Liu, G., Wu, X., Wu, L.: Altdiffusion: A multilingual text-to-image diffusion model. arXiv preprint arXiv:2308.09991 (2023)"},{"key":"3869_CR7","doi-asserted-by":"crossref","unstructured":"Yue, J., Fang, L., Xia, S., Deng, Y., Ma, J.: Dif-fusion: Towards high color fidelity in infrared and visible image fusion with diffusion models. arXiv preprint arXiv:2301.08072 (2023)","DOI":"10.1109\/TIP.2023.3322046"},{"key":"3869_CR8","unstructured":"Wu, J., Fu, R., Fang, H., Zhang, Y., Yang, Y., Xiong, H., Liu, H., Xu, Y.: Medsegdiff: Medical image segmentation with diffusion probabilistic model. In: Medical Imaging with Deep Learning, pp. 1623\u20131639 (2024). PMLR"},{"key":"3869_CR9","unstructured":"Amit, T., Shaharbany, T., Nachmani, E., Wolf, L.: Segdiff: Image segmentation with diffusion probabilistic models. arXiv preprint arXiv:2112.00390 (2021)"},{"key":"3869_CR10","unstructured":"Couairon, G., Verbeek, J., Schwenk, H., Cord, M.: Diffedit: Diffusion-based semantic image editing with mask guidance. arXiv preprint arXiv:2210.11427 (2022)"},{"key":"3869_CR11","doi-asserted-by":"crossref","unstructured":"Chen, T., Wang, C., Shan, H.: Berdiff: Conditional bernoulli diffusion model for medical image segmentation. arXiv preprint arXiv:2304.04429 (2023)","DOI":"10.1007\/978-3-031-43901-8_47"},{"key":"3869_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"3869_CR13","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)"},{"key":"3869_CR14","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"3869_CR15","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"3869_CR16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"3869_CR17","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"3869_CR18","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6569\u20136578 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"3869_CR19","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.: Reppoints: Point set representation for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9657\u20139666 (2019)","DOI":"10.1109\/ICCV.2019.00975"},{"key":"3869_CR20","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"},{"key":"3869_CR21","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229 (2020). Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"3869_CR22","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)"},{"key":"3869_CR23","doi-asserted-by":"crossref","unstructured":"Gao, P., Zheng, M., Wang, X., Dai, J., Li, H.: Fast convergence of detr with spatially modulated co-attention. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3621\u20133630 (2021)","DOI":"10.1109\/ICCV48922.2021.00360"},{"key":"3869_CR24","doi-asserted-by":"crossref","unstructured":"Li, F., Zhang, H., Liu, S., Guo, J., Ni, L.M., Zhang, L.: Dn-detr: Accelerate detr training by introducing query denoising. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13619\u201313627 (2022)","DOI":"10.1109\/CVPR52688.2022.01325"},{"key":"3869_CR25","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., Wang, C., : Sparse r-cnn: End-to-end object detection with learnable proposals. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14454\u201314463 (2021)","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"3869_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: DETRs Beat YOLOs on Real-time Object Detection (2024). arXiv:2304.08069","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"3869_CR27","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458 (2024)"},{"key":"3869_CR28","unstructured":"Khanam, R., Hussain, M.: Yolov11: An overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725 (2024)"},{"key":"3869_CR29","doi-asserted-by":"crossref","unstructured":"Chen, S., Sun, P., Song, Y., Luo, P.: Diffusiondet: Diffusion model for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19830\u201319843 (2023)","DOI":"10.1109\/ICCV51070.2023.01816"},{"key":"3869_CR30","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"3869_CR31","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3869_CR32","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111, 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"3869_CR33","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: 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 (2014). Springer","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"3869_CR34","unstructured":"Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018)"},{"key":"3869_CR35","unstructured":"Wang, X., Chen, K., Huang, Z., Yao, C., Liu, W.: Point linking network for object detection. arXiv preprint arXiv:1706.03646 (2017)"},{"key":"3869_CR36","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., Shum, H.-Y.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022)"},{"key":"3869_CR37","unstructured":"Reis, D., Kupec, J., Hong, J., Daoudi, A.: Real-Time Flying Object Detection with YOLOv8 (2024). arXiv:2305.09972"},{"issue":"1","key":"3869_CR38","doi-asserted-by":"publisher","first-page":"3242","DOI":"10.1038\/s41467-021-23458-5","volume":"12","author":"L Dai","year":"2021","unstructured":"Dai, L., Wu, L., Li, H., Cai, C., Wu, Q., Kong, H., Liu, R., Wang, X., Hou, X., Liu, Y.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 3242 (2021)","journal-title":"Nat. Commun."},{"key":"3869_CR39","doi-asserted-by":"crossref","unstructured":"Ali, S.G., Wang, X., Li, P., Li, H., Yang, P., Jung, Y., Qin, J., Kim, J., Sheng, B.: Egdnet: an efficient glomerular detection network for multiple anomalous pathological feature in glomerulonephritis. The Visual Computer, 1\u201318 (2024)","DOI":"10.1007\/s00371-024-03570-5"},{"key":"3869_CR40","unstructured":"Li, J., Guan, Z., Wang, J., Cheung, C.Y., Zheng, Y., Lim, L.-L., Lim, C.C., Ruamviboonsuk, P., Raman, R., Corsino, L., et al.: Integrated image-based deep learning and language models for primary diabetes care. Nature Medicine, 1\u201311 (2024)"},{"issue":"8","key":"3869_CR41","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/S2213-8587(24)00154-2","volume":"12","author":"B Sheng","year":"2024","unstructured":"Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q.H., Lim, Z.W., Yew, S.M.E., Goh, J.H.L., Bee, Y.M., Sabanayagam, C., Sevdalis, N.: Artificial intelligence for diabetes care: current and future prospects. The Lancet Diabet Endocrinol 12(8), 569\u2013595 (2024)","journal-title":"The Lancet Diabet Endocrinol"},{"issue":"1","key":"3869_CR42","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s41746-024-01204-7","volume":"7","author":"Z Qi","year":"2024","unstructured":"Qi, Z., Li, T., Chen, J., Yam, J.C., Wen, Y., Huang, G., Zhong, H., He, M., Zhu, D., Dai, R.: A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children. NPJ Digit. Med. 7(1), 206 (2024)","journal-title":"NPJ Digit. Med."},{"key":"3869_CR43","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning, pp. 8162\u20138171 (2021). PMLR"},{"key":"3869_CR44","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"3869_CR45","doi-asserted-by":"crossref","unstructured":"Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., Van\u00a0Gool, L.: Diffir: Efficient diffusion model for image restoration. arXiv preprint arXiv:2303.09472 (2023)","DOI":"10.1109\/ICCV51070.2023.01204"},{"key":"3869_CR46","unstructured":"Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. arXiv preprint arXiv:2112.03126 (2021)"},{"issue":"11","key":"3869_CR47","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.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"3869_CR48","unstructured":"Doersch, C.: Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908 (2016)"},{"key":"3869_CR49","doi-asserted-by":"crossref","unstructured":"Gu, Z., Chen, H., Xu, Z.: Diffusioninst: Diffusion model for instance segmentation. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2730\u20132734 (2024). IEEE","DOI":"10.1109\/ICASSP48485.2024.10447191"},{"key":"3869_CR50","doi-asserted-by":"crossref","unstructured":"Xie, F., Wang, Z., Ma, C.: Diffusiontrack: Point set diffusion model for visual object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19113\u201319124 (2024)","DOI":"10.1109\/CVPR52733.2024.01808"},{"key":"3869_CR51","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.3390\/app14052000","volume":"14","author":"L Wang","year":"2024","unstructured":"Wang, L., Jia, J., Dai, H.: Orienteddiffdet: diffusion model for oriented object detection in aerial images. Appl. Sci. 14, 2000 (2024)","journal-title":"Appl. Sci."},{"key":"3869_CR52","doi-asserted-by":"crossref","unstructured":"Chen, Z., Gao, R., Xiang, T.-Z., Lin, F.: Diffusion model for camouflaged object detection. arXiv:2308.00303 (2023)","DOI":"10.3233\/FAIA230302"},{"key":"3869_CR53","unstructured":"Xiang, X., Dr\u00e4ger, S., Zhang, J.: 3diffusiondet: Diffusion model for 3d object detection with robust lidar-camera fusion. arXiv:2311.03742 (2023)"},{"key":"3869_CR54","doi-asserted-by":"crossref","unstructured":"Xu, C., Ling, H., Fidler, S., Litany, O.: 3difftection: 3d object detection with geometry-aware diffusion features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10617\u201310627 (2024)","DOI":"10.1109\/CVPR52733.2024.01010"},{"key":"3869_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, X., Sun, L., Bai, C.: Dpm-det: Diffusion model object detection based on dpm-solver++ guided sampling. In: International Conference on Multimedia Modeling, pp. 379\u2013393 (2024). Springer","DOI":"10.1007\/978-3-031-53308-2_28"},{"issue":"5","key":"3869_CR56","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.3390\/app14052000","volume":"14","author":"L Wang","year":"2024","unstructured":"Wang, L., Jia, J., Dai, H.: Orienteddiffdet: diffusion model for oriented object detection in aerial images. Appl. Sci. 14(5), 2000 (2024)","journal-title":"Appl. Sci."},{"key":"3869_CR57","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256\u20132265 (2015). PMLR"},{"key":"3869_CR58","unstructured":"Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems 32 (2019)"},{"key":"3869_CR59","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1007\/s00037-005-0191-0","volume":"14","author":"L Kristiansen","year":"2005","unstructured":"Kristiansen, L.: Neat function algebraic characterizations of logspace and linspace. Comput. Complex. 14, 72\u201388 (2005)","journal-title":"Comput. Complex."},{"key":"3869_CR60","doi-asserted-by":"crossref","unstructured":"Pratiwi, H., Windarto, A.P., Susliansyah, S., Aria, R.R., Susilowati, S., Rahayu, L.K., Fitriani, Y., Merdekawati, A., Rahadjeng, I.R.: Sigmoid activation function in selecting the best model of artificial neural networks. In: Journal of Physics: Conference Series, vol. 1471, p. 012010 (2020). IOP Publishing","DOI":"10.1088\/1742-6596\/1471\/1\/012010"},{"key":"3869_CR61","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., Girshick, R.: Detectron2 (2019)"},{"key":"3869_CR62","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3869_CR63","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3869_CR64","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"3869_CR65","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"3869_CR66","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"3869_CR67","doi-asserted-by":"crossref","unstructured":"Komer, B., Bergstra, J., Eliasmith, C.: Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. In: ICML Workshop on AutoML, vol. 9, p. 50 (2014). Citeseer Austin, TX","DOI":"10.25080\/Majora-14bd3278-006"},{"key":"3869_CR68","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: Detrs beat yolos on real-time object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16965\u201316974 (2024)","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"3869_CR69","first-page":"30150","volume":"35","author":"T Dockhorn","year":"2022","unstructured":"Dockhorn, T., Vahdat, A., Kreis, K.: Genie: higher-order denoising diffusion solvers. Adv. Neural. Inf. Process. Syst. 35, 30150\u201330166 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3869_CR70","first-page":"5775","volume":"35","author":"C Lu","year":"2022","unstructured":"Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., Zhu, J.: Dpm-solver: a fast ode solver for diffusion probabilistic model sampling in around 10 steps. Adv. Neural. Inf. Process. Syst. 35, 5775\u20135787 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3869_CR71","unstructured":"Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512 (2022)"},{"key":"3869_CR72","unstructured":"Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models. arXiv preprint arXiv:2303.01469 (2023)"},{"key":"3869_CR73","doi-asserted-by":"crossref","unstructured":"Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2836\u20132844 (2021)","DOI":"10.1109\/CVPR46437.2021.00286"},{"key":"3869_CR74","doi-asserted-by":"crossref","unstructured":"Nakayama, K., Uy, M.A., Huang, J., Hu, S., Li, K., Guibas, L.J.: Difffacto: Controllable part-based 3d point cloud generation with cross diffusion. arXiv:2305.01921 (2023)","DOI":"10.1109\/ICCV51070.2023.01311"},{"key":"3869_CR75","unstructured":"Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., Kreis, K.: Lion: Latent point diffusion models for 3d shape generation. arXiv:2210.06978 (2022)"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03869-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-03869-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03869-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T08:25:22Z","timestamp":1757147122000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-03869-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,31]]},"references-count":75,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["3869"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-03869-x","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,31]]},"assertion":[{"value":"2 March 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Delarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}