{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T03:41:43Z","timestamp":1784691703377,"version":"3.55.0"},"publisher-location":"Cham","reference-count":93,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197994","type":"print"},{"value":"9783031198007","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\/978-3-031-19800-7_41","type":"book-chapter","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T12:09:38Z","timestamp":1667909378000},"page":"701-719","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":181,"title":["Self-support Few-Shot Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Qi","family":"Fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Pei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Wing","family":"Tai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi-Keung","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"41_CR1","unstructured":"Allen, K., Shelhamer, E., Shin, H., Tenenbaum, J.: Infinite mixture prototypes for few-shot learning. In: ICML (2019)"},{"key":"41_CR2","unstructured":"Antoniou, A., Edwards, H., Storkey, A.: How to train your MAML. In: ICLR (2019)"},{"key":"41_CR3","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE TPAMI 39, 2481\u20132495 (2017)","journal-title":"IEEE TPAMI"},{"key":"41_CR4","doi-asserted-by":"crossref","unstructured":"Benenson, R., Popov, S., Ferrari, V.: Large-scale interactive object segmentation with human annotators. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01197"},{"key":"41_CR5","unstructured":"Bertinetto, L., Henriques, J.F., Torr, P.H., Vedaldi, A.: Meta-learning with differentiable closed-form solvers. In: ICLR (2019)"},{"key":"41_CR6","doi-asserted-by":"crossref","unstructured":"Boudiaf, M., Kervadec, H., Masud, Z.I., Piantanida, P., Ben Ayed, I., Dolz, J.: Few-shot segmentation without meta-learning: a good transductive inference is all you need? In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01376"},{"key":"41_CR7","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: CVPRW (2019)","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"41_CR8","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE TPAMI 40, 834\u2013848 (2017)","journal-title":"IEEE TPAMI"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.396"},{"key":"41_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with Atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"41_CR11","unstructured":"Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B.: A closer look at few-shot classification. In: ICLR (2019)"},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Cheng, B., et al.: SPGNet: semantic prediction guidance for scene parsing. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00532"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"41_CR14","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: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"41_CR15","unstructured":"Dhillon, G.S., Chaudhari, P., Ravichandran, A., Soatto, S.: A baseline for few-shot image classification. In: ICLR (2019)"},{"key":"41_CR16","unstructured":"Doersch, C., Gupta, A., Zisserman, A.: CrossTransformers: spatially-aware few-shot transfer. In: NeurIPS (2020)"},{"key":"41_CR17","unstructured":"Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: BMVC (2018)"},{"key":"41_CR18","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"83","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes (VOC) challenge. IJCV 83, 303\u2013338 (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"IJCV"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Fan, Q., et al.: Group collaborative learning for co-salient object detection. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01211"},{"key":"41_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-030-58598-3_23","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Q Fan","year":"2020","unstructured":"Fan, Q., Ke, L., Pei, W., Tang, C.-K., Tai, Y.-W.: Commonality-parsing network across shape and appearance for partially supervised instance segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 379\u2013396. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_23"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Fan, Q., Tang, C.K., Tai, Y.W.: Few-shot video object detection. arXiv preprint arXiv:2104.14805 (2021)","DOI":"10.1007\/978-3-031-20044-1_5"},{"key":"41_CR22","doi-asserted-by":"crossref","unstructured":"Fan, Q., Zhuo, W., Tang, C.K., Tai, Y.W.: Few-shot object detection with attention-RPN and multi-relation detector. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00407"},{"key":"41_CR23","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)"},{"key":"41_CR24","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"41_CR25","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Adaptive context network for scene parsing. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00685"},{"key":"41_CR26","doi-asserted-by":"crossref","unstructured":"Gairola, S., Hemani, M., Chopra, A., Krishnamurthy, B.: SimPropNet: improved similarity propagation for few-shot image segmentation. In: IJCAI (2020)","DOI":"10.24963\/ijcai.2020\/80"},{"key":"41_CR27","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"key":"41_CR28","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"41_CR29","unstructured":"Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R.: Meta-learning probabilistic inference for prediction. In: ICLR (2019)"},{"key":"41_CR30","unstructured":"Grant, E., Finn, C., Levine, S., Darrell, T., Griffiths, T.: Recasting gradient-based meta-learning as hierarchical Bayes. In: ICLR (2018)"},{"key":"41_CR31","doi-asserted-by":"crossref","unstructured":"He, H., Zhang, J., Thuraisingham, B., Tao, D.: Progressive one-shot human parsing. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i2.16243"},{"key":"41_CR32","doi-asserted-by":"crossref","unstructured":"He, J., Deng, Z., Zhou, L., Wang, Y., Qiao, Y.: Adaptive pyramid context network for semantic segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00770"},{"key":"41_CR33","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":"41_CR34","unstructured":"Hou, R., Chang, H., Ma, B., Shan, S., Chen, X.: Cross attention network for few-shot classification. In: NeurIPS (2019)"},{"key":"41_CR35","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: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00069"},{"key":"41_CR36","doi-asserted-by":"crossref","unstructured":"Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., Darrell, T.: Few-shot object detection via feature reweighting. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00851"},{"key":"41_CR37","unstructured":"Kim, S., Chikontwe, P., Park, S.H.: Uncertainty-aware semi-supervised few shot segmentation. In: IJCAI (2021)"},{"key":"41_CR38","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., Doll\u00e1r, P.: Panoptic feature pyramid networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00656"},{"key":"41_CR39","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICMLW (2015)"},{"key":"41_CR40","volume-title":"Principles of Gestalt Psychology","author":"K Koffka","year":"1935","unstructured":"Koffka, K.: Principles of Gestalt Psychology. Routledge, Milton Park (1935)"},{"key":"41_CR41","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeurIPS (2012)"},{"key":"41_CR42","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01091"},{"key":"41_CR43","unstructured":"Lee, Y., Choi, S.: Gradient-based meta-learning with learned layerwise metric and subspace. In: ICML (2018)"},{"key":"41_CR44","doi-asserted-by":"crossref","unstructured":"Li, G., Jampani, V., Sevilla-Lara, L., Sun, D., Kim, J..: Adaptive prototype learning and allocation for few-shot segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00823"},{"key":"41_CR45","doi-asserted-by":"crossref","unstructured":"Li, H., Eigen, D., Dodge, S., Zeiler, M., Wang, X.: Finding task-relevant features for few-shot learning by category traversal. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00009"},{"key":"41_CR46","doi-asserted-by":"crossref","unstructured":"Li, W., Wang, L., Xu, J., Huo, J., Gao, Y., Luo, J.: Revisiting local descriptor based image-to-class measure for few-shot learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00743"},{"key":"41_CR47","doi-asserted-by":"crossref","unstructured":"Li, X., Wei, T., Chen, Y.P., Tai, Y.W., Tang, C.K.: FSS-1000: a 1000-class dataset for few-shot segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00294"},{"key":"41_CR48","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"41_CR49","doi-asserted-by":"crossref","unstructured":"Lin, G., Shen, C., Van Den Hengel, A., Reid, I.: Efficient piecewise training of deep structured models for semantic segmentation. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.348"},{"key":"41_CR50","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":"TY 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":"41_CR51","doi-asserted-by":"crossref","unstructured":"Liu, B., Ding, Y., Jiao, J., Ji, X., Ye, Q.: Anti-aliasing semantic reconstruction for few-shot semantic segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00962"},{"key":"41_CR52","doi-asserted-by":"crossref","unstructured":"Liu, C., et al.: Learning a few-shot embedding model with contrastive learning. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i10.17047"},{"key":"41_CR53","doi-asserted-by":"crossref","unstructured":"Liu, L., Cao, J., Liu, M., Guo, Y., Chen, Q., Tan, M.: Dynamic extension nets for few-shot semantic segmentation. In: ACM MM (2020)","DOI":"10.1145\/3394171.3413915"},{"key":"41_CR54","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhang, C., Lin, G., Liu, F.: CRNet: cross-reference networks for few-shot segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00422"},{"key":"41_CR55","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-030-58545-7_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Zhang, X., Zhang, S., He, X.: Part-aware prototype network for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 142\u2013158. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_9"},{"key":"41_CR56","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"41_CR57","doi-asserted-by":"crossref","unstructured":"Lu, Z., He, S., Zhu, X., Zhang, L., Song, Y.Z., Xiang, T.: Simpler is better: few-shot semantic segmentation with classifier weight transformer. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00862"},{"key":"41_CR58","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1126\/science.159.3810.56","volume":"159","author":"RK Merton","year":"1968","unstructured":"Merton, R.K.: The Matthew effect in science: the reward and communication systems of science are considered. Science 159, 56\u201363 (1968)","journal-title":"Science"},{"key":"41_CR59","doi-asserted-by":"crossref","unstructured":"Min, J., Kang, D., Cho, M.: Hypercorrelation squeeze for few-shot segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00686"},{"key":"41_CR60","doi-asserted-by":"crossref","unstructured":"Nguyen, K., Todorovic, S.: Feature weighting and boosting for few-shot segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00071"},{"key":"41_CR61","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"41_CR62","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1007\/978-3-030-58526-6_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Ouyang","year":"2020","unstructured":"Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with Superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 762\u2013780. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_45"},{"key":"41_CR63","doi-asserted-by":"crossref","unstructured":"Qi, H., Brown, M., Lowe, D.G.: Low-shot learning with imprinted weights. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00610"},{"key":"41_CR64","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"41_CR65","unstructured":"Rusu, A.A., et al.: Meta-learning with latent embedding optimization. In: ICLR (2019)"},{"key":"41_CR66","doi-asserted-by":"crossref","unstructured":"Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation. In: BMVC (2017)","DOI":"10.5244\/C.31.167"},{"key":"41_CR67","doi-asserted-by":"crossref","unstructured":"Siam, M., Doraiswamy, N., Oreshkin, B.N., Yao, H., Jagersand, M.: Weakly supervised few-shot object segmentation using co-attention with visual and semantic embeddings. In: IJCAI (2020)","DOI":"10.24963\/ijcai.2020\/120"},{"key":"41_CR68","doi-asserted-by":"crossref","unstructured":"Siam, M., Oreshkin, B.N., Jagersand, M.: AMP: adaptive masked proxies for few-shot segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00535"},{"key":"41_CR69","doi-asserted-by":"crossref","unstructured":"Tian, P., Wu, Z., Qi, L., Wang, L., Shi, Y., Gao, Y.: Differentiable meta-learning model for few-shot semantic segmentation. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6887"},{"key":"41_CR70","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1109\/TPAMI.2020.3013717","volume":"44","author":"Z Tian","year":"2020","unstructured":"Tian, Z., Zhao, H., Shu, M., Yang, Z., Li, R., Jia, J.: Prior guided feature enrichment network for few-shot segmentation. IEEE TPAMI 44, 1050\u20131065 (2020)","journal-title":"IEEE TPAMI"},{"key":"41_CR71","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1007\/978-3-030-58601-0_43","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Wang","year":"2020","unstructured":"Wang, H., Zhang, X., Hu, Y., Yang, Y., Cao, X., Zhen, X.: Few-shot semantic segmentation with democratic attention networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 730\u2013746. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58601-0_43"},{"key":"41_CR72","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANet: few-shot image semantic segmentation with prototype alignment. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00929"},{"key":"41_CR73","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"41_CR74","doi-asserted-by":"crossref","unstructured":"Wu, Z., Shi, X., Lin, G., Cai, J.: Learning meta-class memory for few-shot semantic segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00056"},{"key":"41_CR75","doi-asserted-by":"crossref","unstructured":"Xie, G.S., Liu, J., Xiong, H., Shao, L.: Scale-aware graph neural network for few-shot semantic segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00543"},{"key":"41_CR76","doi-asserted-by":"crossref","unstructured":"Xie, G.S., Xiong, H., Liu, J., Yao, Y., Shao, L.: Few-shot semantic segmentation with cyclic memory network. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00720"},{"key":"41_CR77","doi-asserted-by":"crossref","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: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00967"},{"key":"41_CR78","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1007\/978-3-030-58598-3_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Yang","year":"2020","unstructured":"Yang, B., Liu, C., Li, B., Jiao, J., Ye, Q.: Prototype mixture models for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 763\u2013778. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_45"},{"key":"41_CR79","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhuo, W., Qi, L., Shi, Y., Gao, Y.: Mining latent classes for few-shot segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00860"},{"key":"41_CR80","unstructured":"Yang, X., et al.: BriNet: towards bridging the intra-class and inter-class gaps in one-shot segmentation. In: BMVC (2020)"},{"key":"41_CR81","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.75"},{"key":"41_CR82","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-030-58539-6_11","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Yuan","year":"2020","unstructured":"Yuan, Y., Chen, X., Wang, J.: Object-contextual representations for semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 173\u2013190. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_11"},{"key":"41_CR83","doi-asserted-by":"crossref","unstructured":"Zhang, B., Xiao, J., Qin, T.: Self-guided and cross-guided learning for few-shot segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00821"},{"key":"41_CR84","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., Yao, R.: Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00968"},{"key":"41_CR85","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Yao, R., Shen, C.: CANet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00536"},{"key":"41_CR86","doi-asserted-by":"crossref","unstructured":"Zhang, F., et al.: ACFNet: attentional class feature network for semantic segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00690"},{"key":"41_CR87","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 \u2013 ECCV 2020","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":"41_CR88","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"41_CR89","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1007\/978-3-030-01240-3_17","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Zhao","year":"2018","unstructured":"Zhao, H., et al.: PSANet: point-wise spatial attention network for scene parsing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 270\u2013286. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_17"},{"key":"41_CR90","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.544"},{"key":"41_CR91","doi-asserted-by":"crossref","unstructured":"Zhu, K., Zhai, W., Zha, Z.J., Cao, Y.: Self-supervised tuning for few-shot segmentation. In: IJCAI (2020)","DOI":"10.24963\/ijcai.2020\/142"},{"key":"41_CR92","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.: Asymmetric non-local neural networks for semantic segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00068"},{"key":"41_CR93","doi-asserted-by":"crossref","unstructured":"Zhuge, Y., Shen, C.: Deep reasoning network for few-shot semantic segmentation. In: ACM MM (2021)","DOI":"10.1145\/3474085.3475658"}],"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\/978-3-031-19800-7_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T12:21:53Z","timestamp":1667910113000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19800-7_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197994","9783031198007"],"references-count":93,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19800-7_41","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":"9 November 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)"}}]}}