{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T18:41:45Z","timestamp":1726080105844},"publisher-location":"Berlin, Heidelberg","reference-count":31,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783662615096"},{"type":"electronic","value":"9783662615102"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-662-61510-2_6","type":"book-chapter","created":{"date-parts":[[2020,4,11]],"date-time":"2020-04-11T06:02:25Z","timestamp":1586584945000},"page":"57-69","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DPNet: A Dual Path Network for Road Scene Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Lu","family":"Ye","sequence":"first","affiliation":[]},{"given":"Jiayi","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Wujie","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Sugianto","family":"Sugianto","sequence":"additional","affiliation":[]},{"given":"George Kofi","family":"Agordzo","sequence":"additional","affiliation":[]},{"given":"Derrick","family":"Yeboah","sequence":"additional","affiliation":[]},{"given":"Mukonde Tonderayi","family":"Kevin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,12]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Intelligence, 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":"6_CR2","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017). \narXiv:1706.05587"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1925\u20131934 (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"6_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.115","author":"X Guo","year":"2019","unstructured":"Guo, X., et al.: GAN-based virtual-to-real image translation for urban scene semantic segmentation. Neurocomputing. (2019). \nhttps:\/\/doi.org\/10.1016\/j.neucom.2019.01.115","journal-title":"Neurocomputing."},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neucom.2018.03.037","volume":"304","author":"H Yu","year":"2018","unstructured":"Yu, H., et al.: Methods and datasets on semantic segmentation: a review. Neurocomputing 304, 82\u2013103 (2018)","journal-title":"Neurocomputing"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"6_CR7","unstructured":"Quan, T.M., Hildebrand, D.G., Jeong, W.K.: Fusionnet: a deep fully residual convolutional neural network for image segmentation in connectomics (2016). \narXiv:1612.05360"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Wang, P., et al.: Understanding convolution for semantic segmentation. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 1451\u20131460 (2018)","DOI":"10.1109\/WACV.2018.00163"},{"key":"6_CR9","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, 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR10","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions (2015). \narXiv:1511.07122"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, R., Tang, S., Zhang, Y., Li, J., Yan, S.: Scale-adaptive convolutions for scene parsing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2031\u20132039 (2017)","DOI":"10.1109\/ICCV.2017.224"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Learning a discriminative feature network for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1857\u20131866 (2018)","DOI":"10.1109\/CVPR.2018.00199"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W.: SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5659\u20135667 (2017)","DOI":"10.1109\/CVPR.2017.667"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"6_CR15","unstructured":"Mnih, V., Heess, N., Graves, A.: Recurrent models of visual attention. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 2204\u20132212 (2014)"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H.: Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156\u20133164 (2017)","DOI":"10.1109\/CVPR.2017.683"},{"key":"6_CR17","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3640\u20133649 (2016)","DOI":"10.1109\/CVPR.2016.396"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Fowlkes, C.C.: Laplacian pyramid reconstruction and refinement for semantic segmentation. In: Proceedings of the European Conference on Computer Vision, pp. 519\u2013534 (2016)","DOI":"10.1007\/978-3-319-46487-9_32"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR20","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"6_CR21","unstructured":"Ibtehaz, N., Rahman, M.S.: MultiResUNet: Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation (2019). \narXiv:1902.04049"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Berman, M., Triki, A.R., Blaschko, M.B.: The Lov\u00e1sz-Softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4413\u20134421 (2018)","DOI":"10.1109\/CVPR.2018.00464"},{"key":"6_CR23","first-page":"547","volume":"37","author":"P Jaccard","year":"1901","unstructured":"Jaccard, P.: \u00c9tude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaudoise Sci Nat. 37, 547\u2013579 (1901)","journal-title":"Bull Soc Vaudoise Sci Nat."},{"key":"6_CR24","unstructured":"Bach, F.: Learning with submodular functions: A convex optimization perspective. Found. Trends\u00ae Mach. Learn. 6, 145\u2013373 (2013)"},{"key":"6_CR25","unstructured":"Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian SegNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding (2015). \narXiv:1511.02680"},{"key":"6_CR26","unstructured":"Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: a deep neural network architecture for real-time semantic segmentation (2016). \narXiv:1606.02147"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: Proceedings of the IEEE Visual Communications and Image Processing, pp. 1\u20134 (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"6_CR28","unstructured":"Iglovikov, V., Shvets, A.: TernausNet: U-net with VGG11 encoder pre-trained on imagenet for image segmentation (2018). \narXiv:1801.05746"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Visin, F., et al.: ReSeg: a recurrent neural network-based model for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 41\u201348 (2016)","DOI":"10.1109\/CVPRW.2016.60"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Li, H., Xiong, P., Fan, H., Sun, J.: DFANet: deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9522\u20139531 (2019)","DOI":"10.1109\/CVPR.2019.00975"},{"key":"6_CR31","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Learning a discriminative feature network for semantic segmentation (2018). \narXiv:1804.09337"}],"container-title":["Lecture Notes in Computer Science","Transactions on Edutainment XVI"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-662-61510-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,11]],"date-time":"2020-04-11T06:03:19Z","timestamp":1586584999000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-662-61510-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783662615096","9783662615102"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-662-61510-2_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}