{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:23:50Z","timestamp":1761582230485,"version":"3.40.3"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200434"},{"type":"electronic","value":"9783031200441"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20044-1_18","type":"book-chapter","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T23:12:10Z","timestamp":1666221130000},"page":"310-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Time-rEversed DiffusioN tEnsor Transformer: A New TENET of\u00a0Few-Shot Object Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5531-3296","authenticated-orcid":false,"given":"Shan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7032-0403","authenticated-orcid":false,"given":"Naila","family":"Murray","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0961-0441","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6340-5289","authenticated-orcid":false,"given":"Piotr","family":"Koniusz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"18_CR1","unstructured":"Exponentiation by squaring. Wikipedia. https:\/\/en.wikipedia.org\/wiki\/Exponentiation_by_squaring. Accessed 12 Mar 2021"},{"key":"18_CR2","unstructured":"Tsallis entropy. Wikipedia. https:\/\/en.wikipedia.org\/wiki\/Tsallis_entropy. Accessed 12 Mar 2021"},{"key":"18_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"18_CR4","unstructured":"Chen, H., Wang, Y., Wang, G., Qiao, Y.: LSTD: a low-shot transfer detector for object detection. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, 2\u20137 February 2018, pp. 2836\u20132843. AAAI Press (2018)"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20\u201325 June 2009, Miami, Florida, USA, pp. 248\u2013255. IEEE Computer Society (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"2","key":"18_CR6","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int. J. Comput. Vis."},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Fan, Q., Zhuo, W., Tai, Y.: Few-shot object detection with attention-rpn and multi-relation detector. CoRR abs\/1908.01998 (2019)","DOI":"10.1109\/CVPR42600.2020.00407"},{"key":"18_CR8","doi-asserted-by":"publisher","unstructured":"Girshick, R.B.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7\u201313 December 2015, pp. 1440\u20131448. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"18_CR9","doi-asserted-by":"publisher","unstructured":"Hu, H., Zhang, Z., Xie, Z., Lin, S.: Local relation networks for image recognition. In: ICCV 2019, pp. 3463\u20133472. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00356","DOI":"10.1109\/ICCV.2019.00356"},{"issue":"8","key":"18_CR10","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2011\u20132023 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR11","doi-asserted-by":"publisher","unstructured":"Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., Darrell, T.: Few-shot object detection via feature reweighting. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27\u2013November 2, 2019, pp. 8419\u20138428. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00851","DOI":"10.1109\/ICCV.2019.00851"},{"key":"18_CR12","doi-asserted-by":"publisher","unstructured":"Karlinsky, L., et al.: Repmet: representative-based metric learning for classification and few-shot object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 5197\u20135206. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00534","DOI":"10.1109\/CVPR.2019.00534"},{"key":"18_CR13","doi-asserted-by":"publisher","unstructured":"Kong, T., Yao, A., Chen, Y., Sun, F.: Hypernet: towards accurate region proposal generation and joint object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 845\u2013853. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.98","DOI":"10.1109\/CVPR.2016.98"},{"key":"18_CR14","doi-asserted-by":"publisher","unstructured":"Koniusz, P., Tas, Y., Porikli, F.: Domain adaptation by mixture of alignments of second-or higher-order scatter tensors. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 7139\u20137148. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.755","DOI":"10.1109\/CVPR.2017.755"},{"key":"18_CR15","unstructured":"Koniusz, P., Wang, L., Cherian, A.: Tensor representations for action recognition. In: TPAMI (2020)"},{"key":"18_CR16","unstructured":"Koniusz, P., Yan, F., Gosselin, P.H., Mikolajczyk, K.: Higher-order occurrence pooling on mid-and low-level features: Visual concept detection. Tech, Report (2013)"},{"issue":"2","key":"18_CR17","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1109\/TPAMI.2016.2545667","volume":"39","author":"P Koniusz","year":"2017","unstructured":"Koniusz, P., Yan, F., Gosselin, P., Mikolajczyk, K.: Higher-order occurrence pooling for bags-of-words: visual concept detection. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 313\u2013326 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2545667","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR18","unstructured":"Koniusz, P., Zhang, H.: Power normalizations in fine-grained image, few-shot image and graph classification. In: TPAMI (2020)"},{"key":"18_CR19","doi-asserted-by":"publisher","unstructured":"Koniusz, P., Zhang, H., Porikli, F.: A deeper look at power normalizations. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 5774\u20135783. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00605","DOI":"10.1109\/CVPR.2018.00605"},{"issue":"6","key":"18_CR20","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"key":"18_CR21","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1137\/S0895479896305696","volume":"21","author":"LD Lathauwer","year":"2000","unstructured":"Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21, 1253\u20131278 (2000)","journal-title":"SIAM J. Matrix Anal. Appl."},{"issue":"4","key":"18_CR22","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3905\/jpm.2004.110","volume":"30","author":"O Ledoit","year":"2004","unstructured":"Ledoit, O., Wolf, M.: Honey, i shrunk the sample covariance matrix. J. Portfolio Manage. 30(4), 110\u2013119 (2004). https:\/\/doi.org\/10.3905\/jpm.2004.110","journal-title":"J. Portfolio Manage."},{"key":"18_CR23","doi-asserted-by":"publisher","unstructured":"Lee, H., Lee, M., Kwak, N.: Few-shot object detection by attending to per-sample-prototype. In: WACV, 2022, Waikoloa, HI, USA, 3\u20138 January 2022, pp. 1101\u20131110. IEEE (2022). https:\/\/doi.org\/10.1109\/WACV51458.2022.00117","DOI":"10.1109\/WACV51458.2022.00117"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Li, A., Li, Z.: Transformation invariant few-shot object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3094\u20133102 (2021)","DOI":"10.1109\/CVPR46437.2021.00311"},{"key":"18_CR25","doi-asserted-by":"publisher","unstructured":"Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 510\u2013519. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00060","DOI":"10.1109\/CVPR.2019.00060"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Few-shot object detection via classification refinement and distractor retreatment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15395\u201315403 (2021)","DOI":"10.1109\/CVPR46437.2021.01514"},{"key":"18_CR27","doi-asserted-by":"publisher","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 936\u2013944. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"18_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"18_CR29","unstructured":"Lu, J., et al.: SOFT: softmax-free transformer with linear complexity. CoRR abs\/2110.11945 (2021)"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Rahman, S., Wang, L., Sun, C., Zhou, L.: Redro: efficiently learning large-sized spd visual representation. In: European Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-58555-6_1"},{"key":"18_CR31","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 6517\u20136525. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"18_CR32","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. CoRR abs\/1804.02767 (2018)"},{"key":"18_CR33","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 7\u201312 December 2015, Montreal, Quebec, Canada, pp. 91\u201399 (2015)"},{"key":"18_CR34","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-540-45167-9_12","volume-title":"Learning Theory and Kernel Machines","author":"AJ Smola","year":"2003","unstructured":"Smola, A.J., Kondor, R.: Kernels and regularization on graphs. In: Sch\u00f6lkopf, B., Warmuth, M.K. (eds.) COLT-Kernel 2003. LNCS (LNAI), vol. 2777, pp. 144\u2013158. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-45167-9_12"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Sun, B., Li, B., Cai, S., Yuan, Y., Zhang, C.: FSCE: few-shot object detection via contrastive proposal encoding. CoRR abs\/2103.05950 (2021)","DOI":"10.1109\/CVPR46437.2021.00727"},{"key":"18_CR36","doi-asserted-by":"publisher","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7\u201312 June 2015, pp. 1\u20139. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"18_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/11744047_45","volume-title":"Computer Vision","author":"O Tuzel","year":"2006","unstructured":"Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589\u2013600. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744047_45"},{"key":"18_CR38","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 2017, pp. 5998\u20136008 (2017)"},{"key":"18_CR39","unstructured":"Wang, X., Huang, T.E., Gonzalez, J., Darrell, T., Yu, F.: Frustratingly simple few-shot object detection. In: ICML 2020. Proceedings of Machine Learning Research, vol. 119, pp. 9919\u20139928. PMLR (2020)"},{"key":"18_CR40","unstructured":"West, J., Venture, D., Warnick, S.: Spring research presentation: a theoretical foundation for inductive transfer. Brigham Young Univ. College Phys. Math. Sci. (2007). https:\/\/web.archive.org\/web\/20070801120743\/http:\/\/cpms.byu.edu\/springresearch\/abstract-entry?id=861"},{"issue":"3","key":"18_CR41","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1037\/h0074898","volume":"8","author":"RS Woodworth","year":"1901","unstructured":"Woodworth, R.S., Thorndike, E.L.: The influence of improvement in one mental function upon the efficiency of other functions. Psychol. Rev. (I) 8(3), 247\u2013261 (1901). https:\/\/doi.org\/10.1037\/h0074898","journal-title":"Psychol. Rev. (I)"},{"key":"18_CR42","doi-asserted-by":"crossref","unstructured":"Wu, A., Han, Y., Zhu, L., Yang, Y.: Universal-prototype enhancing for few-shot object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9567\u20139576, October 2021","DOI":"10.1109\/ICCV48922.2021.00943"},{"key":"18_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1007\/978-3-030-58517-4_27","volume-title":"Computer Vision","author":"J Wu","year":"2020","unstructured":"Wu, J., Liu, S., Huang, D., Wang, Y.: Multi-scale positive sample refinement for few-shot object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 456\u2013472. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_27"},{"key":"18_CR44","doi-asserted-by":"publisher","unstructured":"Xie, S., Girshick, R.B., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 5987\u20135995. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"18_CR45","doi-asserted-by":"publisher","unstructured":"Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., Lin, L.: Meta R-CNN: towards general solver for instance-level low-shot learning. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27\u2013November 2, 2019, pp. 9576\u20139585. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00967","DOI":"10.1109\/ICCV.2019.00967"},{"key":"18_CR46","unstructured":"Yang, Y., Wei, F., Shi, M., Li, G.: Restoring negative information in few-shot object detection. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6\u201312 December 2020, virtual (2020)"},{"key":"18_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-030-58558-7_31","volume-title":"Computer Vision","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Zhang, L., Qi, X., Li, H., Torr, P.H.S., Koniusz, P.: Few-shot action recognition with permutation-invariant attention. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 525\u2013542. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_31"},{"key":"18_CR48","doi-asserted-by":"publisher","unstructured":"Zhang, H., Koniusz, P.: Power normalizing second-order similarity network for few-shot learning. In: IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, HI, USA, 7\u201311 January 2019, pp. 1185\u20131193. IEEE (2019). https:\/\/doi.org\/10.1109\/WACV.2019.00131","DOI":"10.1109\/WACV.2019.00131"},{"key":"18_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, H., Koniusz, P., Jian, S., Li, H., Torr, P.H.S.: Rethinking class relations: absolute-relative supervised and unsupervised few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9432\u20139441, June 2021","DOI":"10.1109\/CVPR46437.2021.00931"},{"key":"18_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, S., Luo, D., Wang, L., Koniusz, P.: Few-shot object detection by second-order pooling. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69538-5_23"},{"key":"18_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, L., Murray, N., Koniusz, P.: Kernelized few-shot object detection with efficient integral aggregation. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01861"},{"key":"18_CR52","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: ICLR 2021. OpenReview.net (2021)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20044-1_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T04:28:38Z","timestamp":1728188918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20044-1_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200434","9783031200441"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20044-1_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}