{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:54:44Z","timestamp":1774965284584,"version":"3.50.1"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200557","type":"print"},{"value":"9783031200564","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\/978-3-031-20056-4_2","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T19:31:54Z","timestamp":1667417514000},"page":"20-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["UCTNet: Uncertainty-Aware Cross-Modal Transformer Network for\u00a0Indoor RGB-D Semantic Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7245-5878","authenticated-orcid":false,"given":"Xiaowen","family":"Ying","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0117-0621","authenticated-orcid":false,"given":"Mooi Choo","family":"Chuah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"issue":"12","key":"2_CR1","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 Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Cao, J., Leng, H., Lischinski, D., Cohen-Or, D., Tu, C., Li, Y.: ShapeConv: shape-aware convolutional layer for indoor RGB-D semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7088\u20137097 (2021)","DOI":"10.1109\/ICCV48922.2021.00700"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1\u20134. IEEE (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"4296","DOI":"10.1109\/TIP.2020.2968250","volume":"29","author":"C Chen","year":"2020","unstructured":"Chen, C., Wei, J., Peng, C., Zhang, W., Qin, H.: Improved saliency detection in RGB-D images using two-phase depth estimation and selective deep fusion. IEEE Trans. Image Process. 29, 4296\u20134307 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR5","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint arXiv:1412.7062 (2014)"},{"issue":"4","key":"2_CR6","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 Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR7","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"2_CR8","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":"2_CR9","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1109\/TIP.2021.3049332","volume":"30","author":"LZ Chen","year":"2021","unstructured":"Chen, L.Z., Lin, Z., Wang, Z., Yang, Y.L., Cheng, M.M.: Spatial information guided convolution for real-time RGBD semantic segmentation. IEEE Trans. Image Process. 30, 2313\u20132324 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"2_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-3-030-58621-8_33","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Chen","year":"2020","unstructured":"Chen, X., et al.: Bi-directional cross-modality feature propagation with separation-and-aggregation gate for RGB-D semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 561\u2013577. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_33"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Y., Mensink, T., Gavves, E.: 3D neighborhood convolution: Learning depth-aware features for RGB-D and RGB semantic segmentation. In: 2019 International Conference on 3D Vision (3DV), pp. 173\u2013182. IEEE (2019)","DOI":"10.1109\/3DV.2019.00028"},{"key":"2_CR12","unstructured":"Chu, X., Zhang, B., Tian, Z., Wei, X., Xia, H.: Do we really need explicit position encodings for vision transformers? arXiv e-prints pp. arXiv-2102 (2021)"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Ding, H., Jiang, X., Shuai, B., Liu, A.Q., Wang, G.: Context contrasted feature and gated multi-scale aggregation for scene segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2393\u20132402 (2018)","DOI":"10.1109\/CVPR.2018.00254"},{"key":"2_CR14","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2650\u20132658 (2015)","DOI":"10.1109\/ICCV.2015.304"},{"key":"2_CR16","unstructured":"Fooladgar, F., Kasaei, S.: Multi-modal attention-based fusion model for semantic segmentation of RGB-depth images. arXiv preprint arXiv:1912.11691 (2019)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Wang, Y., Zhou, J., Wang, C., Lu, H.: Stacked deconvolutional network for semantic segmentation. IEEE Trans. Image Process. (2019)","DOI":"10.1109\/TIP.2019.2895460"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Fu, K., Fan, D.P., Ji, G.P., Zhao, Q.: JL-DCF: joint learning and densely-cooperative fusion framework for RGB-D salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3052\u20133062 (2020)","DOI":"10.1109\/CVPR42600.2020.00312"},{"key":"2_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/978-3-319-46487-9_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Ghiasi","year":"2016","unstructured":"Ghiasi, G., Fowlkes, C.C.: Laplacian pyramid reconstruction and refinement for semantic segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 519\u2013534. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_32"},{"key":"2_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-319-10584-0_23","volume-title":"Computer Vision \u2013 ECCV 2014","author":"S Gupta","year":"2014","unstructured":"Gupta, S., Girshick, R., Arbel\u00e1ez, P., Malik, J.: Learning rich features from RGB-D images for object detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 345\u2013360. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10584-0_23"},{"key":"2_CR21","unstructured":"Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. arXiv preprint arXiv:2103.00112 (2021)"},{"key":"2_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-319-54181-5_14","volume-title":"Computer Vision \u2013 ACCV 2016","author":"C Hazirbas","year":"2017","unstructured":"Hazirbas, C., Ma, L., Domokos, C., Cremers, D.: FuseNet: incorporating depth into semantic segmentation via fusion-based CNN Architecture. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10111, pp. 213\u2013228. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54181-5_14"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"He, J., Deng, Z., Qiao, Y.: Dynamic multi-scale filters for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3562\u20133572 (2019)","DOI":"10.1109\/ICCV.2019.00366"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"He, J., Deng, Z., Zhou, L., Wang, Y., Qiao, Y.: Adaptive pyramid context network for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7519\u20137528 (2019)","DOI":"10.1109\/CVPR.2019.00770"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Hu, X., Yang, K., Fei, L., Wang, K.: ACNet: attention based network to exploit complementary features for RGBD semantic segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1440\u20131444. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803025"},{"key":"2_CR26","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-1-4471-4640-7_8","volume-title":"Consumer Depth Cameras for Computer Vision","author":"A Janoch","year":"2013","unstructured":"Janoch, A., et al.: A category-level 3D object dataset: putting the kinect to work. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds.) Consumer Depth Cameras for Computer Vision. Advances in Computer Vision and Pattern Recognition, pp. 141\u2013165. Springer, London (2013). https:\/\/doi.org\/10.1007\/978-1-4471-4640-7_8"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Ji, W., et al.: Calibrated RGB-D salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9471\u20139481 (2021)","DOI":"10.1109\/CVPR46437.2021.00935"},{"key":"2_CR28","unstructured":"Jiang, J., Zheng, L., Luo, F., Zhang, Z.: RedNet: residual encoder-decoder network for indoor RGB-D semantic segmentation. arXiv preprint arXiv:1806.01054 (2018)"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: ACM SIGGRAPH 2004 Papers, pp. 689\u2013694 (2004)","DOI":"10.1145\/1015706.1015780"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2_CR33","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"2_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-030-58610-2_21","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Luo","year":"2020","unstructured":"Luo, A., Li, X., Yang, F., Jiao, Z., Cheng, H., Lyu, S.: Cascade graph neural networks for RGB-D salient object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 346\u2013364. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_21"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520\u20131528 (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"2_CR36","unstructured":"Park, N., Kim, S.: How do vision transformers work? In: International Conference on Learning Representations (2022)"},{"key":"2_CR37","unstructured":"Park, S.J., Hong, K.S., Lee, S.: RDFNet: RGB-D multi-level residual feature fusion for indoor semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4980\u20134989 (2017)"},{"key":"2_CR38","unstructured":"Paszke, A., et al.: PyTorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, pp. 8026\u20138037 (2019)"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Piao, Y., Ji, W., Li, J., Zhang, M., Lu, H.: Depth-induced multi-scale recurrent attention network for saliency detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7254\u20137263 (2019)","DOI":"10.1109\/ICCV.2019.00735"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Seichter, D., K\u00f6hler, M., Lewandowski, B., Wengefeld, T., Gross, H.M.: Efficient RGB-D semantic segmentation for indoor scene analysis. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13525\u201313531. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561675"},{"key":"2_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1007\/978-3-642-33715-4_54","volume-title":"Computer Vision \u2013 ECCV 2012","author":"N Silberman","year":"2012","unstructured":"Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746\u2013760. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33715-4_54"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"Song, S., Lichtenberg, S.P., Xiao, J.: Sun RGB-D: a RGB-D scene understanding benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 567\u2013576 (2015)","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"2_CR43","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, W., Wang, H., Li, S., Li, X.: Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1407\u20131417 (2021)","DOI":"10.1109\/CVPR46437.2021.00146"},{"key":"2_CR44","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J\u00e9gou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347\u201310357. PMLR (2021)"},{"issue":"5","key":"2_CR45","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1007\/s11263-019-01188-y","volume":"128","author":"A Valada","year":"2020","unstructured":"Valada, A., Mohan, R., Burgard, W.: Self-supervised model adaptation for multimodal semantic segmentation. Int. J. Comput. Vision 128(5), 1239\u20131285 (2020)","journal-title":"Int. J. Comput. Vision"},{"key":"2_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-030-01252-6_9","volume-title":"Computer Vision \u2013 ECCV 2018","author":"W Wang","year":"2018","unstructured":"Wang, W., Neumann, U.: Depth-aware CNN for RGB-D segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 144\u2013161. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_9"},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. arXiv preprint arXiv:2102.12122 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"2_CR48","unstructured":"Xia, X., Kulis, B.: W-Net: a deep model for fully unsupervised image segmentation. arXiv preprint arXiv:1711.08506 (2017)"},{"key":"2_CR49","doi-asserted-by":"crossref","unstructured":"Xiao, J., Owens, A., Torralba, A.: Sun3D: a database of big spaces reconstructed using SFM and object labels. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1625\u20131632 (2013)","DOI":"10.1109\/ICCV.2013.458"},{"key":"2_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/978-3-030-01228-1_26","volume-title":"Computer Vision \u2013 ECCV 2018","author":"T Xiao","year":"2018","unstructured":"Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 432\u2013448. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_26"},{"key":"2_CR51","doi-asserted-by":"crossref","unstructured":"Xing, Y., Wang, J., Chen, X., Zeng, G.: 2.5D convolution for RGB-D semantic segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1410\u20131414. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803757"},{"key":"2_CR52","doi-asserted-by":"crossref","unstructured":"Xing, Y., Wang, J., Chen, X., Zeng, G.: Coupling two-stream RGB-D semantic segmentation network by idempotent mappings. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1850\u20131854. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803146"},{"key":"2_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/978-3-030-58529-7_33","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Xing","year":"2020","unstructured":"Xing, Y., Wang, J., Zeng, G.: Malleable 2.5D convolution: learning receptive fields along the depth-axis for RGB-D scene parsing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 555\u2013571. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58529-7_33"},{"key":"2_CR54","doi-asserted-by":"crossref","unstructured":"Yuan, L., et al.: Tokens-to-token ViT: training vision transformers from scratch on ImageNet. arXiv preprint arXiv:2101.11986 (2021)","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"2_CR55","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"}],"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\/978-3-031-20056-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T00:20:57Z","timestamp":1667607657000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20056-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200557","9783031200564"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20056-4_2","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":"3 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)"}}]}}