{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:47:58Z","timestamp":1779382078491,"version":"3.53.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62264015"],"award-info":[{"award-number":["No.62264015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation of the Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data","award":["No.IPBED17"],"award-info":[{"award-number":["No.IPBED17"]}]},{"name":"Project of Shaanxi Provincial Department of Education, China","award":["No. 20JK983"],"award-info":[{"award-number":["No. 20JK983"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11760-023-02719-4","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T03:02:49Z","timestamp":1692327769000},"page":"129-135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Pruning DETR: efficient end-to-end object detection with sparse structured pruning"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5275-3739","authenticated-orcid":false,"given":"Huaiyuan","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuili","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xve","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"2719_CR1","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference On Computer Vision. p. 1440\u20138 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"2719_CR2","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. 28 (2015)"},{"key":"2719_CR3","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: IEEE you only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA. p. 779\u201388 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2719_CR4","doi-asserted-by":"crossref","unstructured":"Duan, K. W., Bai, S., Xie, L. X., Qi, H. G., Huang, Q. M., Tian, Q. et al.: CenterNet: Keypoint triplets for object detection. In: IEEE\/CVF International Conference on Computer Vision (ICCV). Seoul, SOUTH KOREA. p. 6568\u201377 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"2719_CR5","doi-asserted-by":"publisher","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","volume":"29","author":"T Kong","year":"2020","unstructured":"Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: FoveaBox: beyound anchor-based object detection. IEEE Trans. Image Process. 29, 7389\u20137398 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.3002345","journal-title":"IEEE Trans. Image Process."},{"key":"2719_CR6","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: FCOS: Fully convolutional one-stage object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). p. 9626\u201335 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"2719_CR7","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Advances in neural information processing systems. 2017;30."},{"key":"2719_CR8","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: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I 16: Springer p. 213\u201329 (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"2719_CR9","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:201004159. (2020)"},{"issue":"32","key":"2719_CR10","doi-asserted-by":"publisher","first-page":"15849","DOI":"10.1073\/pnas.1903070116","volume":"116","author":"M Belkin","year":"2019","unstructured":"Belkin, M., Hsu, D., Ma, S., Mandal, S.: Reconciling modern machine-learning practice and the classical bias\u2013variance trade-off. Proc. Natl. Acad. Sci. 116(32), 15849\u201315854 (2019)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"2719_CR11","unstructured":"Zheng, M., Gao, P., Zhang, R., Li, K., Wang, X., Li, H., et al.: End-to-end object detection with adaptive clustering transformer. arXiv preprint arXiv:201109315. (2020)"},{"key":"2719_CR12","doi-asserted-by":"crossref","unstructured":"Wang, T., Yuan, L., Chen, Y., Feng, J., Yan, S.: Pnp-detr: towards efficient visual analysis with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. p. 4661\u201370 (2021)","DOI":"10.1109\/ICCV48922.2021.00462"},{"key":"2719_CR13","unstructured":"Roh, B., Shin, J., Shin, W., Kim, S.: Sparse detr: efficient end-to-end object detection with learnable sparsity. arXiv preprint arXiv:211114330. (2021)"},{"key":"2719_CR14","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. p. 3621\u201330 (2021)","DOI":"10.1109\/ICCV48922.2021.00360"},{"key":"2719_CR15","doi-asserted-by":"crossref","unstructured":"Meng, D., Chen, X., Fan, Z., Zeng, G., Li, H., Yuan, Y., et al.: Conditional detr for fast training convergence. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision p. 3651\u201360 (2021)","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"2719_CR16","unstructured":"Chen, X., Wei, F., Zeng, G., Wang, J.: Conditional detr v2: efficient detection transformer with box queries. arXiv preprint arXiv:220708914 (2022)"},{"key":"2719_CR17","unstructured":"Hanson, S., Pratt, L.: Comparing biases for minimal network construction with back-propagation. In: Advances in neural information processing systems. 1 (1988)"},{"key":"2719_CR18","unstructured":"LeCun, Y., Denker, J., Solla, S.: Optimal brain damage. In: Advances in neural information processing systems. 2 (1989)"},{"key":"2719_CR19","unstructured":"Hassibi, B., Stork, D.: Second order derivatives for network pruning: Optimal brain surgeon. In: Advances in neural information processing systems 5 (1992)"},{"key":"2719_CR20","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in neural information processing systems. 28 (2015)"},{"issue":"3","key":"2719_CR21","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1145\/3007787.3001163","volume":"44","author":"S Han","year":"2016","unstructured":"Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M.A., et al.: EIE: Efficient inference engine on compressed deep neural network. ACM SIGARCH Comput. Archit. News 44(3), 243\u2013254 (2016)","journal-title":"ACM SIGARCH Comput. Archit. News"},{"key":"2719_CR22","unstructured":"Han, S., Mao, H., Dally, WJ.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:151000149. (2015)"},{"key":"2719_CR23","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. p. 770\u20138 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2719_CR24","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer vision\u2013ECCV 2016: 14th European Conference, Proceedings, Part IV 14","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Computer vision\u2013ECCV 2016: 14th European Conference, Proceedings, Part IV 14, pp. 630\u2013645. Springer, Amsterdam, The Netherlands (2016)"},{"key":"2719_CR25","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. p. 1492\u2013500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"2719_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision. p. 2736\u201344 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"2719_CR27","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, N.: Data-driven sparse structure selection for deep neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV). p. 304\u201320 (2018)","DOI":"10.1007\/978-3-030-01270-0_19"},{"key":"2719_CR28","doi-asserted-by":"crossref","unstructured":"Bello, I., Zoph, B., Vaswani, A., Shlens, J., Le, QV.: Attention augmented convolutional networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. p. 3286\u201395 (2019)","DOI":"10.1109\/ICCV.2019.00338"},{"key":"2719_CR29","unstructured":"Ding, X., Chen, H., Zhang, X., Huang, K., Han, J., Ding, G.: Re-parameterizing your optimizers rather than architectures. arXiv:2205.15242; (2022)"},{"key":"2719_CR30","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:160904747. (2016)"},{"key":"2719_CR31","unstructured":"Li, H., Lin, Z.: Accelerated proximal gradient methods for nonconvex programming. In: Advances in neural information processing systems. 28 (2015)"},{"key":"2719_CR32","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Boulanger-Lewandowski, N., Pascanu, R.: Advances in optimizing recurrent networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing: IEEE. p. 8624\u20138 (2013)","DOI":"10.1109\/ICASSP.2013.6639349"},{"key":"2719_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. p. 6848\u201356 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"2719_CR34","unstructured":"Molchanov, P., Tyree, S., Karras, T., Aila, T., Kautz, J.: Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:161106440. (2016)"},{"key":"2719_CR35","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision. p. 1389\u201397 (2017)","DOI":"10.1109\/ICCV.2017.155"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02719-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02719-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02719-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:35:40Z","timestamp":1706178940000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02719-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,18]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2719"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02719-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3071897\/v1","asserted-by":"object"}]},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,18]]},"assertion":[{"value":"16 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"I solemnly declare: I abide by academic ethics, advocating rigorous style of study. The graduation thesis submitted is the result of my independent research under the guidance of my supervisor. This paper does not contain anything that has been published or written by others, except as expressly stated and quoted in the paper. The paper is written by myself and I am responsible for the content written.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}