{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T10:14:13Z","timestamp":1782209653327,"version":"3.54.5"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61620106008"],"award-info":[{"award-number":["61620106008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The national youth talent support program"},{"DOI":"10.13039\/501100006606","name":"Tianjin Natural Science Foundation","doi-asserted-by":"crossref","award":["17JCJQJC43700"],"award-info":[{"award-number":["17JCJQJC43700"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100006606","name":"Tianjin Natural Science Foundation","doi-asserted-by":"crossref","award":["18ZXZNGX00110"],"award-info":[{"award-number":["18ZXZNGX00110"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major Project for New Generation of AI","award":["2018AAA0100400"],"award-info":[{"award-number":["2018AAA0100400"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s11263-021-01539-8","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T09:02:31Z","timestamp":1638176551000},"page":"179-198","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Semantic Edge Detection with Diverse Deep Supervision"],"prefix":"10.1007","volume":"130","author":[{"given":"Yun","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5550-8758","authenticated-orcid":false,"given":"Ming-Ming","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deng-Ping","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Wang","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dacheng","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"1539_CR1","doi-asserted-by":"crossref","unstructured":"Acuna, D., Kar, A., & Fidler, S. (2019). Devil is in the edges: Learning semantic boundaries from noisy annotations. In IEEE conference on computer vision and pattern recognition (pp. 11075\u201311083).","DOI":"10.1109\/CVPR.2019.01133"},{"issue":"1","key":"1539_CR2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s11263-014-0752-2","volume":"112","author":"MR Amer","year":"2015","unstructured":"Amer, M. R., Yousefi, S., Raich, R., & Todorovic, S. (2015). Monocular extraction of 2.1D sketch using constrained convex optimization. International Journal of Computer Vision, 112(1), 23\u201342.","journal-title":"International Journal of Computer Vision"},{"issue":"5","key":"1539_CR3","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbel\u00e1ez","year":"2011","unstructured":"Arbel\u00e1ez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 898\u2013916.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1539_CR4","doi-asserted-by":"crossref","unstructured":"Bertasius, G., Shi, J., & Torresani, L. (2015a). DeepEdge: A multi-scale bifurcated deep network for top-down contour detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4380\u20134389).","DOI":"10.1109\/CVPR.2015.7299067"},{"key":"1539_CR5","doi-asserted-by":"crossref","unstructured":"Bertasius, G., Shi, J., & Torresani, L. (2015b). High-for-low and low-for-high: Efficient boundary detection from deep object features and its applications to high-level vision. In Proceedings of the IEEE international conference on computer vision (pp. 504\u2013512).","DOI":"10.1109\/ICCV.2015.65"},{"key":"1539_CR6","doi-asserted-by":"crossref","unstructured":"Bertasius, G., Shi, J., & Torresani, L. (2016). Semantic segmentation with boundary neural fields. In Proceedings of the IEEE conference on computer vision and pattern recognition  (pp. 3602\u20133610).","DOI":"10.1109\/CVPR.2016.392"},{"key":"1539_CR7","doi-asserted-by":"publisher","first-page":"2548","DOI":"10.1007\/s11263-021-01484-6","volume":"129","author":"J-W Bian","year":"2021","unstructured":"Bian, J.-W., Zhan, H., Wang, N., Li, Z., Zhang, L., Shen, C., et al. (2021). Unsupervised scale-consistent depth learning from video. International Journal of Computer Vision, 129, 2548\u20132564.","journal-title":"International Journal of Computer Vision"},{"issue":"6","key":"1539_CR8","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679\u2013698.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"12","key":"1539_CR9","doi-asserted-by":"publisher","first-page":"5017","DOI":"10.1109\/TIP.2015.2475625","volume":"24","author":"T-H Chan","year":"2015","unstructured":"Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015). PCANet: A simple deep learning baseline for image classification? IEEE Transactions on Image Processing, 24(12), 5017\u20135032.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1539_CR10","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Barron, J.\u00a0T., Papandreou, G., Murphy, K., & Yuille, A.\u00a0L. (2016). Semantic image segmentation with task-specific edge detection using CNNs and a discriminatively trained domain transform. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4545\u20134554).","DOI":"10.1109\/CVPR.2016.492"},{"key":"1539_CR11","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., et\u00a0al. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213\u20133223).","DOI":"10.1109\/CVPR.2016.350"},{"key":"1539_CR12","doi-asserted-by":"crossref","unstructured":"Deng, R., Shen, C., Liu, S., Wang, H., & Liu, X. (2018). Learning to predict crisp boundaries. In European conference on computer vision (pp. 570\u2013586).","DOI":"10.1007\/978-3-030-01231-1_35"},{"issue":"8","key":"1539_CR13","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TPAMI.2014.2377715","volume":"37","author":"P Doll\u00e1r","year":"2015","unstructured":"Doll\u00e1r, P., & Zitnick, C. L. (2015). Fast edge detection using structured forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1558\u20131570.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"1539_CR14","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/TPAMI.2007.1144","volume":"30","author":"V Ferrari","year":"2008","unstructured":"Ferrari, V., Fevrier, L., Jurie, F., & Schmid, C. (2008). Groups of adjacent contour segments for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(1), 36\u201351.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"1539_CR15","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/s11263-009-0270-9","volume":"87","author":"V Ferrari","year":"2010","unstructured":"Ferrari, V., Jurie, F., & Schmid, C. (2010). From images to shape models for object detection. International Journal of Computer Vision, 87(3), 284\u2013303.","journal-title":"International Journal of Computer Vision"},{"key":"1539_CR16","doi-asserted-by":"crossref","unstructured":"Ganin, Y., & Lempitsky, V. (2014). N$$^4$$-Fields: Neural network nearest neighbor fields for image transforms. In Asian conference on computer vision (pp. 536\u2013551).","DOI":"10.1007\/978-3-319-16808-1_36"},{"issue":"11","key":"1539_CR17","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1109\/83.469938","volume":"4","author":"RC Hardie","year":"1995","unstructured":"Hardie, R. C., & Boncelet, C. G. (1995). Gradient-based edge detection using nonlinear edge enhancing prefilters. IEEE Transactions on Image Processing, 4(11), 1572\u20131577.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1539_CR18","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Bourdev, L., Maji, S., & Malik, J. (2011). Semantic contours from inverse detectors. In Proceedings of the IEEE international conference on computer vision (pp. 991\u2013998).","DOI":"10.1109\/ICCV.2011.6126343"},{"key":"1539_CR19","doi-asserted-by":"crossref","unstructured":"Hayder, Z., He, X., & Salzmann, M. (2017). Boundary-aware instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5696\u20135704).","DOI":"10.1109\/CVPR.2017.70"},{"key":"1539_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"1539_CR21","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1109\/83.499917","volume":"5","author":"PV Henstock","year":"1996","unstructured":"Henstock, P. V., & Chelberg, D. M. (1996). Automatic gradient threshold determination for edge detection. IEEE Transactions on Image Processing, 5(5), 784\u2013787.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"7","key":"1539_CR22","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527\u20131554.","journal-title":"Neural Computation"},{"issue":"4","key":"1539_CR23","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1109\/TPAMI.2018.2815688","volume":"41","author":"Q Hou","year":"2019","unstructured":"Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., & Torr, P. (2019). Deeply supervised salient object detection with short connections. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4), 815\u2013828.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1539_CR24","unstructured":"Hou, Q., Liu, J., Cheng, M.-M., Borji, A., & Torr, P.\u00a0H. (2018). Three birds one stone: A unified framework for salient object segmentation, edge detection and skeleton extraction. arXiv preprint arXiv:1803.09860."},{"issue":"2018","key":"1539_CR25","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.neucom.2018.05.088","volume":"313","author":"X Hu","year":"2018","unstructured":"Hu, X., Liu, Y., Wang, K., & Ren, B. (2018). Learning hybrid convolutional features for edge detection. Neurocomputing, 313(2018), 377\u2013385.","journal-title":"Neurocomputing"},{"key":"1539_CR26","doi-asserted-by":"crossref","unstructured":"Hu, Y., Chen, Y., Li, X., & Feng, J. (2019). Dynamic feature fusion for semantic edge detection. In International joint conferences on artificial intelligence (pp. 782\u2013788).","DOI":"10.24963\/ijcai.2019\/110"},{"key":"1539_CR27","unstructured":"Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In The international conference on machine learning (pp. 448\u2013456)."},{"key":"1539_CR28","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., et\u00a0al. (2014). Caffe: Convolutional architecture for fast feature embedding. In ACM international conference on multimedia (pp. 675\u2013678).","DOI":"10.1145\/2647868.2654889"},{"key":"1539_CR29","doi-asserted-by":"crossref","unstructured":"Khoreva, A., Benenson, R., Omran, M., Hein, M., & Schiele, B. (2016). Weakly supervised object boundaries. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 183\u2013192).","DOI":"10.1109\/CVPR.2016.27"},{"key":"1539_CR30","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., & Rother, C. (2017). Instancecut: From edges to instances with multicut. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5008\u20135017).","DOI":"10.1109\/CVPR.2017.774"},{"key":"1539_CR31","unstructured":"Kokkinos, I. (2016). Pushing the boundaries of boundary detection using deep learning. In The international conference on learning representations (pp. 1\u201312)."},{"issue":"1","key":"1539_CR32","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/TPAMI.2003.1159946","volume":"25","author":"S Konishi","year":"2003","unstructured":"Konishi, S., Yuille, A. L., Coughlan, J. M., & Zhu, S. C. (2003). Statistical edge detection: Learning and evaluating edge cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(1), 57\u201374.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1539_CR33","unstructured":"Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2015). Deeply-supervised nets. In Artificial intelligence and statistics (pp. 562\u2013570)."},{"key":"1539_CR34","doi-asserted-by":"crossref","unstructured":"Lim, J.\u00a0J., Zitnick, C.\u00a0L., & Doll\u00e1r, P. (2013). Sketch tokens: A learned mid-level representation for contour and object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3158\u20133165).","DOI":"10.1109\/CVPR.2013.406"},{"key":"1539_CR35","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117\u20132125).","DOI":"10.1109\/CVPR.2017.106"},{"issue":"2","key":"1539_CR36","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T-Y Lin","year":"2020","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Doll\u00e1r, P. (2020). Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318\u2013327.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1539_CR37","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., et\u00a0al. (2014). Microsoft COCO: Common objects in context. In European conference on computer vision (pp. 740\u2013755).","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1539_CR38","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., et\u00a0al. (2016). SSD: Single shot multibox detector. In European conference on computer vision (pp. 21\u201337).","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"8","key":"1539_CR39","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1109\/TPAMI.2018.2878849","volume":"41","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Cheng, M.-M., Hu, X., Bian, J.-W., Zhang, L., Bai, X., et al. (2019). Richer convolutional features for edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8), 1939\u20131946.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1539_CR40","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cheng, M.-M., Hu, X., Wang, K., & Bai, X. (2017). Richer convolutional features for edge detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3000\u20133009).","DOI":"10.1109\/CVPR.2017.622"},{"key":"1539_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Y., Jiang, P.-T., Petrosyan, V., Li, S.-J., Bian, J., Zhang, L., et\u00a0al. (2018). DEL: Deep embedding learning for efficient image segmentation. In International joint conferences on artificial intelligence (pp. 864\u2013870).","DOI":"10.24963\/ijcai.2018\/120"},{"issue":"11","key":"1539_CR42","doi-asserted-by":"publisher","first-page":"5475","DOI":"10.1109\/TIP.2018.2857448","volume":"27","author":"M Mafi","year":"2018","unstructured":"Mafi, M., Rajaei, H., Cabrerizo, M., & Adjouadi, M. (2018). A robust edge detection approach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering. IEEE Transactions on Image Processing, 27(11), 5475\u20135490.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"4","key":"1539_CR43","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1109\/TPAMI.2017.2700300","volume":"40","author":"K-K Maninis","year":"2017","unstructured":"Maninis, K.-K., Pont-Tuset, J., Arbelaez, P., & Van Gool, L. (2017). Convolutional oriented boundaries: From image segmentation to high-level tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 819\u2013833.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"1539_CR44","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TPAMI.2004.1273918","volume":"26","author":"DR Martin","year":"2004","unstructured":"Martin, D. R., Fowlkes, C. C., & Malik, J. (2004). Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5), 530\u2013549.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1539_CR45","unstructured":"Nair, V., & Hinton, G.\u00a0E. (2010). Rectified linear units improve restricted Boltzmann machines. In The international conference on machine learning (pp. 807\u2013814)."},{"key":"1539_CR46","doi-asserted-by":"crossref","unstructured":"Ramalingam, S., Bouaziz, S., Sturm, P., & Brand, M. (2010). Skyline2gps: Localization in urban canyons using omni-skylines. In The IEEE\/RSJ international conference on intelligent robots and systems (pp. 3816\u20133823).","DOI":"10.1109\/IROS.2010.5649105"},{"key":"1539_CR47","doi-asserted-by":"crossref","unstructured":"Shan, Q., Curless, B., Furukawa, Y., Hernandez, C., & Seitz, S.\u00a0M. (2014). Occluding contours for multi-view stereo. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4002\u20134009).","DOI":"10.1109\/CVPR.2014.511"},{"key":"1539_CR48","unstructured":"Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z. (2015). DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3982\u20133991)."},{"issue":"10","key":"1539_CR49","doi-asserted-by":"publisher","first-page":"4962","DOI":"10.1109\/TIP.2017.2726190","volume":"26","author":"P-L Shui","year":"2017","unstructured":"Shui, P.-L., & Wang, F.-P. (2017). Anti-impulse-noise edge detection via anisotropic morphological directional derivatives. IEEE Transactions on Image Processing, 26(10), 4962\u20134977.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1539_CR50","unstructured":"Sobel, I. (1970). Camera models and machine perception. Technical report, Stanford Univercity California, Department of Computer Science."},{"issue":"1","key":"1539_CR51","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929\u20131958.","journal-title":"Journal of Machine Learning Research"},{"key":"1539_CR52","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et\u00a0al. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1\u20139).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1539_CR53","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V., & Fidler, S. (2019). Gated-SCNN: Gated shape CNNs for semantic segmentation. In Proceedings of the IEEE international conference on computer vision (pp. 5229\u20135238).","DOI":"10.1109\/ICCV.2019.00533"},{"issue":"7","key":"1539_CR54","doi-asserted-by":"publisher","first-page":"3385","DOI":"10.1109\/TIP.2016.2642781","volume":"26","author":"P Tang","year":"2017","unstructured":"Tang, P., Wang, X., Feng, B., & Liu, W. (2017). Learning multi-instance deep discriminative patterns for image classification. IEEE Transactions on Image Processing, 26(7), 3385\u20133396.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"2","key":"1539_CR55","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1109\/83.217230","volume":"2","author":"PE Trahanias","year":"1993","unstructured":"Trahanias, P. E., & Venetsanopoulos, A. N. (1993). Color edge detection using vector order statistics. IEEE Transactions on Image Processing, 2(2), 259\u2013264.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1539_CR56","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., & Lu, H. (2015). Visual tracking with fully convolutional networks. In Proceedings of the IEEE international conference on computer vision (pp. 3119\u20133127).","DOI":"10.1109\/ICCV.2015.357"},{"issue":"3","key":"1539_CR57","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TIP.2018.2874279","volume":"28","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Zhao, X., Li, Y., & Huang, K. (2019). Deep crisp boundaries: From boundaries to higher-level tasks. IEEE Transactions on Image Processing, 28(3), 1285\u20131298.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1539_CR58","doi-asserted-by":"crossref","unstructured":"Xie, S., & Tu, Z. (2015). Holistically-nested edge detection. In Proceedings of the IEEE international conference on computer vision (pp. 1395\u20131403).","DOI":"10.1109\/ICCV.2015.164"},{"issue":"1\u20133","key":"1539_CR59","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11263-017-1004-z","volume":"125","author":"S Xie","year":"2017","unstructured":"Xie, S., & Tu, Z. (2017). Holistically-nested edge detection. International Journal of Computer Vision, 125(1\u20133), 3\u201318.","journal-title":"International Journal of Computer Vision"},{"key":"1539_CR60","doi-asserted-by":"crossref","unstructured":"Yang, J., Price, B., Cohen, S., Lee, H., & Yang, M.-H. (2016). Object contour detection with a fully convolutional encoder\u2013decoder network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 193\u2013202).","DOI":"10.1109\/CVPR.2016.28"},{"issue":"12","key":"1539_CR61","doi-asserted-by":"publisher","first-page":"5895","DOI":"10.1109\/TIP.2017.2750403","volume":"26","author":"W Yang","year":"2017","unstructured":"Yang, W., Feng, J., Yang, J., Zhao, F., Liu, J., Guo, Z., et al. (2017). Deep edge guided recurrent residual learning for image super-resolution. IEEE Transactions on Image Processing, 26(12), 5895\u20135907.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1539_CR62","unstructured":"Yu, F., & Koltun, V. (2016). Multi-scale context aggregation by dilated convolutions. In International conference on learning representations (pp. 1\u201313)."},{"key":"1539_CR63","doi-asserted-by":"crossref","unstructured":"Yu, Z., Feng, C., Liu, M.-Y., & Ramalingam, S. (2017). CASENet: Deep category-aware semantic edge detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5964\u20135973).","DOI":"10.1109\/CVPR.2017.191"},{"key":"1539_CR64","doi-asserted-by":"crossref","unstructured":"Yu, Z., Liu, W., Zou, Y., Feng, C., Ramalingam, S., Kumar, B., et\u00a0al. (2018). Simultaneous edge alignment and learning. In European conference on computer vision (pp. 400\u2013417).","DOI":"10.1007\/978-3-030-01219-9_24"},{"key":"1539_CR65","doi-asserted-by":"crossref","unstructured":"Zamir, A.\u00a0R., Sax, A., Shen, W., Guibas, L., Malik, J., & Savarese, S. (2018). Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3712\u20133722).","DOI":"10.1109\/CVPR.2018.00391"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-021-01539-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-021-01539-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-021-01539-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T20:06:14Z","timestamp":1641499574000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-021-01539-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,29]]},"references-count":65,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1539"],"URL":"https:\/\/doi.org\/10.1007\/s11263-021-01539-8","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,29]]},"assertion":[{"value":"21 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}