{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T13:58:49Z","timestamp":1770213529684,"version":"3.49.0"},"publisher-location":"Cham","reference-count":73,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198052","type":"print"},{"value":"9783031198069","type":"electronic"}],"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\/978-3-031-19806-9_40","type":"book-chapter","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T23:11:54Z","timestamp":1666221114000},"page":"692-716","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Augmenting Deep Classifiers with\u00a0Polynomial Neural Networks"],"prefix":"10.1007","author":[{"given":"Grigorios G.","family":"Chrysos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markos","family":"Georgopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiankang","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean","family":"Kossaifi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yannis","family":"Panagakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anima","family":"Anandkumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"40_CR1","first-page":"2773","volume":"15","author":"A Anandkumar","year":"2014","unstructured":"Anandkumar, A., Ge, R., Hsu, D., Kakade, S.M., Telgarsky, M.: Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15, 2773\u20132832 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"40_CR2","unstructured":"Balduzzi, D., Frean, M., Leary, L., Lewis, J., Ma, K.W.D., McWilliams, B.: The shattered gradients problem: If resnets are the answer, then what is the question? In: International Conference on Machine Learning (ICML), pp. 342\u2013350 (2017)"},{"key":"40_CR3","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: Delving into high quality object detection. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: International Conference on Computer Vision Workshops (ICCV\u2019W), pp. 0\u20130 (2019)","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"40_CR6","unstructured":"Chen, Y., Kalantidis, Y., Li, J., Yan, S., Feng, J.: A$$\\hat{}$$ 2-nets: Double attention networks. In: Advances in neural information processing systems (NeurIPS), pp. 352\u2013361 (2018)"},{"key":"40_CR7","unstructured":"Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. In: Advances in neural information processing systems (NeurIPS), pp. 4467\u20134475 (2017)"},{"key":"40_CR8","unstructured":"Chrysos, G., Georgopoulos, M., Panagakis, Y.: Conditional generation using polynomial expansions. In: Advances in neural information processing systems (NeurIPS) (2021)"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Chrysos, G., Moschoglou, S., Bouritsas, G., Panagakis, Y., Deng, J., Zafeiriou, S.: $$\\pi -$$nets: Deep polynomial neural networks. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00735"},{"key":"40_CR10","unstructured":"Chrysos, G., Moschoglou, S., Panagakis, Y., Zafeiriou, S.: Polygan: High-order polynomial generators. arXiv preprint arXiv:1908.06571 (2019)"},{"key":"40_CR11","unstructured":"Cohen, N., Shashua, A.: Convolutional rectifier networks as generalized tensor decompositions. In: International Conference on Machine Learning (ICML), pp. 955\u2013963. PMLR (2016)"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"40_CR14","unstructured":"Galassi, A., Lippi, M., Torroni, P.: Attention, please! a critical review of neural attention models in natural language processing. arXiv preprint arXiv:1902.02181 (2019)"},{"key":"40_CR15","unstructured":"Gao, S., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.H.: Res2net: A new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) (2019)"},{"key":"40_CR16","unstructured":"Georgopoulos, M., Chrysos, G., Pantic, M., Panagakis, Y.: Multilinear latent conditioning for generating unseen attribute combinations. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Georgopoulos, M., Oldfield, J., Nicolaou, M.A., Panagakis, Y., Pantic, M.: Mitigating demographic bias in facial datasets with style-based multi-attribute transfer. In: International Journal of Computer Vision (IJCV) (2021)","DOI":"10.1007\/s11263-021-01448-w"},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Girdhar, R., Carreira, J., Doersch, C., Zisserman, A.: Video action transformer network. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 244\u2013253 (2019)","DOI":"10.1109\/CVPR.2019.00033"},{"issue":"10","key":"40_CR19","doi-asserted-by":"publisher","first-page":"992","DOI":"10.3390\/math7100992","volume":"7","author":"B Hanin","year":"2019","unstructured":"Hanin, B.: Universal function approximation by deep neural nets with bounded width and relu activations. Mathematics 7(10), 992 (2019)","journal-title":"Mathematics"},{"key":"40_CR20","unstructured":"Hardt, M., Ma, T.: Identity matters in deep learning. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"40_CR21","unstructured":"Hayashi, K., Yamaguchi, T., Sugawara, Y., Maeda, S.i.: Einconv: Exploring unexplored tensor network decompositions for convolutional neural networks. In: Advances in neural information processing systems (NeurIPS) (2019)"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Sun, J.: Convolutional neural networks at constrained time cost. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5353\u20135360 (2015)","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"40_CR25","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik, K., Stinchcombe, M., White, H., et al.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359\u2013366 (1989)","journal-title":"Neural Netw."},{"key":"40_CR26","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Vedaldi, A.: Gather-excite: Exploiting feature context in convolutional neural networks. In: Advances in neural information processing systems (NeurIPS), pp. 9401\u20139411 (2018)"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"40_CR28","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"40_CR29","doi-asserted-by":"crossref","unstructured":"Huang, L., Yang, D., Lang, B., Deng, J.: Decorrelated batch normalization. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 791\u2013800 (2018)","DOI":"10.1109\/CVPR.2018.00089"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: Ccnet: Criss-cross attention for semantic segmentation. In: International Conference on Computer Vision (ICCV), pp. 603\u2013612 (2019)","DOI":"10.1109\/ICCV.2019.00069"},{"key":"40_CR31","unstructured":"Jayakumar, S.M., et al.: Multiplicative interactions and where to find them. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"40_CR32","unstructured":"Kileel, J., Trager, M., Bruna, J.: On the expressive power of deep polynomial neural networks. In: Advances in neural information processing systems (NeurIPS) (2019)"},{"key":"40_CR33","unstructured":"Kim, J.H., Jun, J., Zhang, B.T.: Bilinear attention networks. In: Advances in neural information processing systems (NeurIPS), pp. 1564\u20131574 (2018)"},{"key":"40_CR34","doi-asserted-by":"crossref","unstructured":"Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3), 455\u2013500 (2009)","DOI":"10.1137\/07070111X"},{"key":"40_CR35","unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: Cifar-100 (canadian institute for advanced research) https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html"},{"key":"40_CR36","unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: The cifar-10 dataset. online: https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html 55 (2014)"},{"key":"40_CR37","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NeurIPS), pp. 1097\u20131105 (2012)"},{"key":"40_CR38","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510\u2013519 (2019)","DOI":"10.1109\/CVPR.2019.00060"},{"key":"40_CR39","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, N., Liu, J., Hou, X.: Factorized bilinear models for image recognition. In: International Conference on Computer Vision (ICCV), pp. 2079\u20132087 (2017)","DOI":"10.1109\/ICCV.2017.229"},{"key":"40_CR40","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Neural architecture search for lightweight non-local networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10297\u201310306 (2020)","DOI":"10.1109\/CVPR42600.2020.01031"},{"key":"40_CR41","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 \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","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: 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":"40_CR42","unstructured":"Livni, R., Shalev-Shwartz, S., Shamir, O.: On the computational efficiency of training neural networks. In: Advances in neural information processing systems (NeurIPS), pp. 855\u2013863 (2014)"},{"key":"40_CR43","doi-asserted-by":"crossref","unstructured":"Lokhande, V.S., Tasneeyapant, S., Venkatesh, A., Ravi, S.N., Singh, V.: Generating accurate pseudo-labels in semi-supervised learning and avoiding overconfident predictions via hermite polynomial activations. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11435\u201311443 (2020)","DOI":"10.1109\/CVPR42600.2020.01145"},{"key":"40_CR44","unstructured":"Ma, X., et al.: A tensorized transformer for language modeling. In: Advances in neural information processing systems (NeurIPS), pp. 2232\u20132242 (2019)"},{"key":"40_CR45","volume-title":"Analysis III: Spaces of Differentiable Functions","author":"S Nikol\u2019skii","year":"2013","unstructured":"Nikol\u2019skii, S.: Analysis III: Spaces of Differentiable Functions. Encyclopaedia of Mathematical Sciences, Springer, Berlin Heidelberg (2013)"},{"key":"40_CR46","unstructured":"Parmar, N., Ramachandran, P., Vaswani, A., Bello, I., Levskaya, A., Shlens, J.: Stand-alone self-attention in vision models. In: Advances in neural information processing systems (NeurIPS), pp. 68\u201380 (2019)"},{"key":"40_CR47","unstructured":"Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. arXiv preprint arXiv:1710.05941 (2017)"},{"key":"40_CR48","unstructured":"Reed, S., Sohn, K., Zhang, Y., Lee, H.: Learning to disentangle factors of variation with manifold interaction. In: International Conference on Machine Learning (ICML), pp. 1431\u20131439 (2014)"},{"key":"40_CR49","unstructured":"Rolnick, D., Tegmark, M.: The power of deeper networks for expressing natural functions. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"40_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/978-3-030-00928-1_48","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"AG Roy","year":"2018","unstructured":"Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel \u2018squeeze & excitation\u2019 in fully convolutional networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 421\u2013429. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_48"},{"key":"40_CR51","doi-asserted-by":"crossref","unstructured":"Ruan, D., Wen, J., Zheng, N., Zheng, M.: Linear context transform block. In: AAAI, pp. 5553\u20135560 (2020)","DOI":"10.1609\/aaai.v34i04.6007"},{"issue":"3","key":"40_CR52","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision (IJCV) 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision (IJCV)"},{"key":"40_CR53","unstructured":"Shamir, O.: Are resnets provably better than linear predictors? In: Advances in neural information processing systems (NeurIPS), pp. 507\u2013516 (2018)"},{"issue":"13","key":"40_CR54","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1109\/TSP.2017.2690524","volume":"65","author":"ND Sidiropoulos","year":"2017","unstructured":"Sidiropoulos, N.D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E.E., Faloutsos, C.: Tensor decomposition for signal processing and machine learning. IEEE Trans. Signal Process. 65(13), 3551\u20133582 (2017)","journal-title":"IEEE Trans. Signal Process."},{"key":"40_CR55","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015)"},{"issue":"5","key":"40_CR56","doi-asserted-by":"publisher","first-page":"237","DOI":"10.2307\/3029337","volume":"21","author":"MH Stone","year":"1948","unstructured":"Stone, M.H.: The generalized weierstrass approximation theorem. Math. Mag. 21(5), 237\u2013254 (1948)","journal-title":"Math. Mag."},{"key":"40_CR57","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/0024-3795(73)90023-2","volume":"6","author":"GP Styan","year":"1973","unstructured":"Styan, G.P.: Hadamard products and multivariate statistical analysis. Linear Algebra Appl. 6, 217\u2013240 (1973)","journal-title":"Linear Algebra Appl."},{"key":"40_CR58","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"40_CR59","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations (ICLR) (2014)"},{"key":"40_CR60","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in neural information processing systems (NeurIPS), pp. 5998\u20136008 (2017)"},{"key":"40_CR61","doi-asserted-by":"crossref","unstructured":"Wang, W., Li, X., Yang, J., Lu, T.: Mixed link networks. In: International Joint Conferences on Artificial Intelligence (IJCAI) (2018)","DOI":"10.24963\/ijcai.2018\/391"},{"key":"40_CR62","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"40_CR63","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Sort: Second-order response transform for visual recognition. In: International Conference on Computer Vision (ICCV), pp. 1359\u20131368 (2017)","DOI":"10.1109\/ICCV.2017.152"},{"key":"40_CR64","unstructured":"Warden, P.: Speech commands: A dataset for limited-vocabulary speech recognition. arXiv preprint arXiv:1804.03209 (2018)"},{"key":"40_CR65","unstructured":"Won, M., Chun, S., Serra, X.: Toward interpretable music tagging with self-attention. arXiv preprint arXiv:1906.04972 (2019)"},{"key":"40_CR66","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"40_CR67","doi-asserted-by":"crossref","unstructured":"Yang, Z., Zhu, L., Wu, Y., Yang, Y.: Gated channel transformation for visual recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11794\u201311803 (2020)","DOI":"10.1109\/CVPR42600.2020.01181"},{"key":"40_CR68","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-030-58555-6_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Yin","year":"2020","unstructured":"Yin, M., et al.: Disentangled non-local neural networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12360, pp. 191\u2013207. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58555-6_12"},{"key":"40_CR69","doi-asserted-by":"crossref","unstructured":"Yu, Z., Yu, J., Fan, J., Tao, D.: Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In: International Conference on Computer Vision (ICCV), pp. 1821\u20131830 (2017)","DOI":"10.1109\/ICCV.2017.202"},{"key":"40_CR70","unstructured":"Zaeemzadeh, A., Rahnavard, N., Shah, M.: Norm-preservation: Why residual networks can become extremely deep? arXiv preprint arXiv:1805.07477 (2018)"},{"key":"40_CR71","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)","DOI":"10.5244\/C.30.87"},{"key":"40_CR72","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: International Conference on Machine Learning (ICML) (2019)"},{"key":"40_CR73","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.: Asymmetric non-local neural networks for semantic segmentation. In: International Conference on Computer Vision (ICCV), pp. 593\u2013602 (2019)","DOI":"10.1109\/ICCV.2019.00068"}],"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\/978-3-031-19806-9_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T23:19:17Z","timestamp":1666394357000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19806-9_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198052","9783031198069"],"references-count":73,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19806-9_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}