{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:49:56Z","timestamp":1778496596813,"version":"3.51.4"},"publisher-location":"Cham","reference-count":80,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585471","type":"print"},{"value":"9783030585488","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58548-8_33","type":"book-chapter","created":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T23:02:42Z","timestamp":1603926162000},"page":"562-580","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":111,"title":["Region Graph Embedding Network for Zero-Shot Learning"],"prefix":"10.1007","author":[{"given":"Guo-Sen","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazhou","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Akata, Z., Malinowski, M., Fritz, M., Schiele, B.: Multi-cue zero-shot learning with strong supervision. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.14"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for attribute-based classification. In: CVPR (2013)","DOI":"10.1109\/CVPR.2013.111"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for image classification. In: TPAMI (2016)","DOI":"10.1109\/TPAMI.2015.2487986"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Akata, Z., Reed, S., Walter, D., Lee, H., Schiele, B.: Evaluation of output embeddings for fine-grained image classification. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298911"},{"key":"33_CR5","unstructured":"Annadani, Y., Biswas, S.: Preserving semantic relations for zero-shot learning. In: CVPR (2018)"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Cacheux, Y., Borgne, H., Crucianu, M.: Modeling inter and intra-class relations in the triplet loss for zero-shot learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.01043"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Changpinyo, S., Chao, W.L., Gong, B., Sha, F.: Synthesized classifiers for zero-shot learning. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.575"},{"key":"33_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-319-46475-6_4","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W-L Chao","year":"2016","unstructured":"Chao, W.-L., Changpinyo, S., Gong, B., Sha, F.: An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 52\u201368. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_4"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Liu, W., Chang, S.F.: Zero-shot visual recognition using semantics-preserving adversarial embedding network. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00115"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Elhoseiny, M., Elfeki, M.: Creativity inspired zero-shot learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00588"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Elhoseiny, M., Zhu, Y., Zhang, H., Elgammal, A.M.: Link the head to the \"beak\": zero shot learning from noisy text description at part precision. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.666"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206772"},{"key":"33_CR13","unstructured":"Felix, R., Kumar, V.B., Reid, I., Carneiro, G.: Multi-modal cycle-consistent generalized zero-shot learning. In: ECCV (2008)"},{"key":"33_CR14","unstructured":"Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., T. Mikolov, E.A.: DeViSE: a deep visual-semantic embedding model. In: NeurIPS (2013)"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Fu, Y., Hospedales, T.M., Xiang, T., Gong, S.: Transductive multi-view zero-shot learning. In: TPAMI (2015)","DOI":"10.1109\/TPAMI.2015.2408354"},{"key":"33_CR16","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: NeurIPS (2014)"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"33_CR18","unstructured":"Jayaraman, D., Grauman, K.: Zero-shot recognition with unreliable attributes. In: NeurIPS (2014)"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, R., Shan, S., Chen, X.: Transferable contrastive network for generalized zero-shot learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00986"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Kampffmeyer, M., Chen, Y., Liang, X., Wang, H., Zhang, Y., Xing, E.: Rethinking knowledge graph propagation for zero-shot learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01175"},{"key":"33_CR21","unstructured":"Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016)"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Kodirov, E., Xiang, T., Fu, Z., Gong, S.: Unsupervised domain adaptation for zero-shot learning. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.282"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Kodirov, E., Xiang, T., Gong, S.: Semantic autoencoder for zero-shot learning. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.473"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR (2009)","DOI":"10.1109\/CVPRW.2009.5206594"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, F., Cheng, H., Liu, W., Shen, D.: Contour knowledge transfer for salient object detection. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01267-0_22"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, J., Zhang, J., Huang, K.: Discriminative learning of latent features for zero-shot recognition. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00779"},{"key":"33_CR27","unstructured":"Liu, S., Long, M., Wang, J., Jordan, M.: Generalized zero-shot learning with deep calibration network. In: NeurIPS (2018)"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Y., Guo, J., Cai, D., He, X.: Attribute attention for semantic disambiguation in zero-shot learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00680"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Long, Y., Liu, L., Shen, F., Shao, L., Li, X.: Zero-shot learning using synthesised unseen visual data with diffusion regularisation. In: TPAMI (2017)","DOI":"10.1109\/TPAMI.2017.2762295"},{"key":"33_CR30","doi-asserted-by":"crossref","unstructured":"Lu, X., Wang, W., Ma, C., Shen, J., Shao, L., Porikli, F.: See more, know more: unsupervised video object segmentation with co-attention siamese networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00374"},{"key":"33_CR31","doi-asserted-by":"crossref","unstructured":"Lu, X., Wang, W., Martin, D., Zhou, T., Shen, J., Luc, V.G.: Video object segmentation with episodic graph memory networks. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58580-8_39"},{"key":"33_CR32","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. JMLR 9, 2579\u20132605 (2008)","journal-title":"JMLR"},{"key":"33_CR33","doi-asserted-by":"crossref","unstructured":"Morgado, P., Vasconcelos, N.: Semantically consistent regularization for zero-shot recognition. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.220"},{"key":"33_CR34","unstructured":"Norouzi, M., et al.: Zero-shot learning by convex combination of semantic embeddings. In: NeurIPS (2014)"},{"key":"33_CR35","unstructured":"Palatucci, M., Pomerleau, D., Hinton, G.E., Mitchell, T.M.: Zero-shot learning with semantic output codes. In: NeurIPS (2009)"},{"key":"33_CR36","doi-asserted-by":"crossref","unstructured":"Patterson, G., Hays, J.: Sun attribute database: discovering, annotating, and recognizing scene attributes. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6247998"},{"key":"33_CR37","doi-asserted-by":"crossref","unstructured":"Qiao, R., Liu, L., Shen, C., van den Hengel, A.: Less is more: zero-shot learning from online textual documents with noise suppression. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.247"},{"key":"33_CR38","doi-asserted-by":"crossref","unstructured":"Reed, S., Akata, Z., Lee, H., Schiele, B.: Learning deep representations of fine-grained visual descriptions. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.13"},{"key":"33_CR39","unstructured":"Romera-Paredes, B., Torr, P.: An embarrassingly simple approach to zero-shot learning. In: ICML (2015)"},{"key":"33_CR40","doi-asserted-by":"crossref","unstructured":"Shen, Y., Qin, J., Huang, L., Liu, L., Zhu, F., Shao, L.: Invertible zero-shot recognition flows. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58517-4_36"},{"key":"33_CR41","unstructured":"Socher, R., Ganjoo, M., Manning, C.D., Ng, A.: Zero-shot learning through cross-modal transfer. In: NeurIPS (2013)"},{"key":"33_CR42","doi-asserted-by":"crossref","unstructured":"Song, J., Shen, C., Yang, Y., Liu, Y., Song, M.: Transductive unbiased embedding for zero-shot learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00113"},{"key":"33_CR43","doi-asserted-by":"crossref","unstructured":"Verma, V.K., Arora, G., Mishra, A., Rai, P.: Generalized zero-shot learning via synthesized examples. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00450"},{"key":"33_CR44","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset. In: Technical report (2011)"},{"key":"33_CR45","doi-asserted-by":"crossref","unstructured":"Wang, X., Ye, Y., Gupta, A.: Zero-shot recognition via semantic embeddings and knowledge graphs. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00717"},{"key":"33_CR46","doi-asserted-by":"publisher","first-page":"172683","DOI":"10.1109\/ACCESS.2019.2956775","volume":"7","author":"B Wu","year":"2019","unstructured":"Wu, B., et al.: Tencent ml-images: a large-scale multi-label image database for visual representation learning. IEEE Access 7, 172683\u2013172693 (2019)","journal-title":"IEEE Access"},{"key":"33_CR47","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1007\/s11263-018-1085-3","volume":"126","author":"B Wu","year":"2018","unstructured":"Wu, B., Jia, F., Liu, W., Ghanem, B., Lyu, S.: Multi-label learning with missing labels using mixed dependency graphs. Int. J. Comput. Vis. 126, 875\u2013896 (2018)","journal-title":"Int. J. Comput. Vis."},{"key":"33_CR48","doi-asserted-by":"crossref","unstructured":"Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., Schiele, B.: Latent embeddings for zero-shot classification. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.15"},{"key":"33_CR49","doi-asserted-by":"crossref","unstructured":"Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: Feature generating networks for zero-shot learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00581"},{"key":"33_CR50","doi-asserted-by":"crossref","unstructured":"Xian, Y., Schiele, B., Akata, Z.: Zero-shot learning-the good, the bad and the ugly. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.328"},{"key":"33_CR51","doi-asserted-by":"crossref","unstructured":"Xian, Y., Sharma, S., Saurabh, S., Akata, Z.: f-VAEGAN-D2: a feature generating framework for any-shot learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01052"},{"key":"33_CR52","doi-asserted-by":"crossref","unstructured":"Xie, G.S., et al.: Attentive region embedding network for zero-shot learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00961"},{"key":"33_CR53","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.patcog.2017.06.002","volume":"71","author":"GS Xie","year":"2017","unstructured":"Xie, G.S., Zhang, X.Y., Yang, W., Xu, M., Yan, S., Liu, C.L.: LG-CNN: from local parts to global discrimination for fine-grained recognition. Pattern Recogn. 71, 118\u2013131 (2017)","journal-title":"Pattern Recogn."},{"key":"33_CR54","doi-asserted-by":"publisher","first-page":"4290","DOI":"10.1109\/TNNLS.2019.2953675","volume":"31","author":"GS Xie","year":"2019","unstructured":"Xie, G.S., et al.: SRSC: selective, robust, and supervised constrained feature representation for image classification. IEEE Trans. Neural Netw. Learn. Syst. 31, 4290\u20134302 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"33_CR55","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/978-3-319-46478-7_28","volume-title":"Computer Vision \u2013 ECCV 2016","author":"H Xu","year":"2016","unstructured":"Xu, H., Saenko, K.: Ask, attend and answer: exploring question-guided spatial attention for visual question answering. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 451\u2013466. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_28"},{"key":"33_CR56","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhao, R., Zhu, F., Wang, H., Ouyang, W.: Attention-aware compositional network for person re-identification. arXiv:1805.03344 (2018)","DOI":"10.1109\/CVPR.2018.00226"},{"key":"33_CR57","unstructured":"Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: ICML (2015)"},{"key":"33_CR58","unstructured":"Yang, F.S.Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: CVPR (2018)"},{"key":"33_CR59","doi-asserted-by":"crossref","unstructured":"Yang, G., Liu, J., Xu, J., Li, X.: Dissimilarity representation learning for generalized zero-shot recognition. In: MM (2018)","DOI":"10.1145\/3240508.3240686"},{"key":"33_CR60","first-page":"2348","volume":"31","author":"Y Yao","year":"2020","unstructured":"Yao, Y., et al.: Exploiting web images for multi-output classification: from category to subcategories. IEEE Trans. Neural Netw. Learn. Syst. 31, 2348\u20132360 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"33_CR61","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1109\/TMM.2017.2684626","volume":"19","author":"Y Yao","year":"2017","unstructured":"Yao, Y., Zhang, J., Shen, F., Hua, X., Xu, J., Tang, Z.: Exploiting web images for dataset construction: a domain robust approach. IEEE Trans. Multimedia 19, 1771\u20131784 (2017)","journal-title":"IEEE Trans. Multimedia"},{"key":"33_CR62","doi-asserted-by":"crossref","unstructured":"Ye, M., Guo, Y.: Zero-shot classification with discriminative semantic representation learning. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.542"},{"key":"33_CR63","unstructured":"Yu, H., Lee, B.: Zero-shot learning via simultaneous generating and learning. In: NeurIPS (2019)"},{"key":"33_CR64","unstructured":"Yu, Y., Ji, Z., Fu, Y., Guo, J., Pang, Y., Zhang, Z.: Stacked semantics-guided attention model for fine-grained zero-shot learning. In: NeurIPS (2018)"},{"key":"33_CR65","doi-asserted-by":"crossref","unstructured":"Yu, Y., Ji, Z., Han, J., Zhang, Z.: Episode-based prototype generating network for zero-shot learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01405"},{"key":"33_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xiang, T., Gong, S., et al.: Learning a deep embedding model for zero-shot learning. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.321"},{"key":"33_CR67","doi-asserted-by":"publisher","first-page":"2843","DOI":"10.1109\/TCSVT.2020.2984666","volume":"30","author":"L Zhang","year":"2020","unstructured":"Zhang, L., et al.: Towards effective deep embedding for zero-shot learning. IEEE Trans. Circ. Syst. Video Technol. 30, 2843\u20132852 (2020)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"33_CR68","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s11263-019-01253-6","volume":"128","author":"L Zhang","year":"2020","unstructured":"Zhang, L., et al.: Adaptive importance learning for improving lightweight image super-resolution network. Int. J. Comput. Vis. 128, 479\u2013499 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"33_CR69","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1109\/TCSVT.2018.2842206","volume":"29","author":"L Zhang","year":"2018","unstructured":"Zhang, L., et al.: Unsupervised domain adaptation using robust class-wise matching. IEEE Trans. Circ. Syst. Video Technol. 29, 1339\u20131349 (2018)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"33_CR70","doi-asserted-by":"publisher","first-page":"5969","DOI":"10.1109\/TIP.2018.2862629","volume":"27","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Wei, W., Bai, C., Gao, Y., Zhang, Y.: Exploiting clustering manifold structure for hyperspectral imagery super-resolution. IEEE Trans. Image Process. 27, 5969\u20135982 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"33_CR71","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s11263-018-1080-8","volume":"126","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., Shi, Q.: Cluster sparsity field: an internal hyperspectral imagery prior for reconstruction. Int. J. Comput. Vis. 126, 797\u2013821 (2018)","journal-title":"Int. J. Comput. Vis."},{"key":"33_CR72","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Saligrama, V.: Zero-shot learning via semantic similarity embedding. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.474"},{"key":"33_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Saligrama, V.: Zero-shot learning via joint latent similarity embedding. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.649"},{"key":"33_CR74","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TPAMI.2018.2847335","volume":"41","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liu, L., Shen, F., Shen, H.T., Shao, L.: Binary multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1774\u20131782 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"33_CR75","doi-asserted-by":"crossref","unstructured":"Zhao, F., Liao, S., Xie, G.S., Zhao, J., Zhang, K., Shao, L.: Unsupervised domain adaptation with noise resistible mutual-training for person re-identification. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58621-8_31"},{"key":"33_CR76","doi-asserted-by":"crossref","unstructured":"Zhao, F., Zhao, J., Yan, S., Feng, J.: Dynamic conditional networks for few-shot learning. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01267-0_2"},{"key":"33_CR77","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A.A.L., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"33_CR78","doi-asserted-by":"crossref","unstructured":"Zhu, P., Wang, H., Saligrama, V.: Generalized zero-shot recognition based on visually semantic embedding. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00311"},{"key":"33_CR79","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Elhoseiny, M., Liu, B., Peng, X., Elgammal, A.: A generative adversarial approach for zero-shot learning from noisy texts. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00111"},{"key":"33_CR80","unstructured":"Zhu, Y., Xie, J., Tang, Z., Peng, X., Elgammal, A.: Learning where to look: semantic-guided multi-attention localization for zero-shot learning. In: NeurIPS (2019)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58548-8_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:14:35Z","timestamp":1730160875000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58548-8_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585471","9783030585488"],"references-count":80,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58548-8_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 October 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}