{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:42:19Z","timestamp":1743000139826,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030687892"},{"type":"electronic","value":"9783030687908"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","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":[[2021]]},"DOI":"10.1007\/978-3-030-68790-8_55","type":"book-chapter","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T13:13:24Z","timestamp":1613999604000},"page":"719-735","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Training of Multiple and Mixed Tasks with a Single Network Using Feature Modulation"],"prefix":"10.1007","author":[{"given":"Mana","family":"Takeda","sequence":"first","affiliation":[]},{"given":"Gibran","family":"Benitez","sequence":"additional","affiliation":[]},{"given":"Keiji","family":"Yanai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"55_CR1","doi-asserted-by":"crossref","unstructured":"Bragman, F.J., Tanno, R., Ourselin, S., Alexander, D.C., Cardoso, J.: Stochastic filter groups for multi-task CNNs: learning specialist and generalist convolution kernels. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00147"},{"key":"55_CR2","doi-asserted-by":"crossref","unstructured":"Chang, S., Park, S., Yang, J., Kwak, N.: Sym-parameterized dynamic inference for mixed-domain image translation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00490"},{"key":"55_CR3","unstructured":"Chen, Z., Badrinarayanan, V., Lee, C., Rabinovich, A.: GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: ICML (2018)"},{"key":"55_CR4","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":"55_CR5","doi-asserted-by":"publisher","unstructured":"Dumoulin, V., Perez, E., Schucher, N., Strub, F., Vries, H.d., Courville, A., Bengio, Y.: Feature-wise transformations (2018). https:\/\/doi.org\/10.23915\/distill.00011. https:\/\/distill.pub\/2018\/feature-wise-transformations","DOI":"10.23915\/distill.00011"},{"key":"55_CR6","unstructured":"Dumoulin, V., Shlens, J., Kudlur, M.: A learned representation for artistic style. In: ICLR (2017)"},{"key":"55_CR7","doi-asserted-by":"crossref","unstructured":"Dvornik, N., Shmelkov, K., Mairal, J., Schmid, C.: Blitznet: a real-time deep network for scene understanding. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.447"},{"key":"55_CR8","doi-asserted-by":"crossref","unstructured":"Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.304"},{"key":"55_CR9","doi-asserted-by":"crossref","unstructured":"Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. In: IJCV (2015)","DOI":"10.1007\/s11263-014-0733-5"},{"key":"55_CR10","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., Shlens, J.: Exploring the structure of a real-time, arbitrary neural artistic stylization network. In: BMVC (2017)","DOI":"10.5244\/C.31.114"},{"key":"55_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"55_CR12","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbelaez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: ICCV (2011)","DOI":"10.1109\/ICCV.2011.6126343"},{"key":"55_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"55_CR14","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"55_CR15","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.167"},{"key":"55_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-030-01219-9_11","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Huang","year":"2018","unstructured":"Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179\u2013196. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_11"},{"key":"55_CR17","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"55_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"55_CR19","unstructured":"Kaiser, L., et al.: One model to learn them all. arXiv:1706.05137 (2017)"},{"key":"55_CR20","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"55_CR21","unstructured":"Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: CVPR (2018)"},{"key":"55_CR22","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"55_CR23","doi-asserted-by":"crossref","unstructured":"Kokkinos, I.: UberNet: training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: ICCV (2017)","DOI":"10.1109\/CVPR.2017.579"},{"key":"55_CR24","doi-asserted-by":"crossref","unstructured":"Liu, S., Johns, E., Davison, A.J.: End-to-end multi-task learning with attention. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00197"},{"key":"55_CR25","doi-asserted-by":"crossref","unstructured":"Mallya, A., Davis, D., Lazebnik, S.: Piggyback: adapting a single network to multiple tasks by learning to mask weights. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01225-0_5"},{"key":"55_CR26","doi-asserted-by":"crossref","unstructured":"Maninis, K.K., Radosavovic, I., Kokkinos, I.: Attentive single-tasking of multiple tasks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00195"},{"key":"55_CR27","doi-asserted-by":"crossref","unstructured":"Matsumoto, A., Yanai, K.: Continual learning of an image transformation network using task-dependent weight selection masks. In: ACPR (2019)","DOI":"10.1007\/978-3-030-41299-9_11"},{"key":"55_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-030-01270-0_17","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Guo","year":"2018","unstructured":"Guo, M., Haque, A., Huang, D.-A., Yeung, S., Fei-Fei, L.: Dynamic task prioritization for multitask learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 282\u2013299. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_17"},{"key":"55_CR29","doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.433"},{"key":"55_CR30","doi-asserted-by":"crossref","unstructured":"Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: a review. arXiv:1802.07569 (2018)","DOI":"10.1016\/j.neunet.2019.01.012"},{"key":"55_CR31","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00244"},{"key":"55_CR32","doi-asserted-by":"crossref","unstructured":"Perez, E., Strub, F., De Vries, H., Dumoulin, V., Courville, A.: FiLM: visual reasoning with a general conditioning layer. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"55_CR33","doi-asserted-by":"crossref","unstructured":"Rosenfeld, A., Biparva, M., Tsotsos, J.K.: Priming neural networks. arXiv:1711.05918 (2017)","DOI":"10.1109\/CVPRW.2018.00270"},{"key":"55_CR34","unstructured":"Standley, T., Zamir, A.R., Chen, D., Guibas, L.J., Malik, J., Savarese, S.: Which tasks should be learned together in multi-task learning? arXiv:1905.07553 (2019)"},{"key":"55_CR35","doi-asserted-by":"crossref","unstructured":"Strezoski, G., van Noord, N., Worring, M.: Many task learning with task routing. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00146"},{"key":"55_CR36","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Instance normalization: the missing ingredient for fast stylization. arXiv:1607.08022 (2016)"},{"key":"55_CR37","unstructured":"Vandenhende, S., Brabandere, B.D., Gool, L.V.: Branched multi-task networks: deciding what layers to share. arXiv:1904.02920 (2019)"},{"key":"55_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-030-01246-5_25","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Zhao","year":"2018","unstructured":"Zhao, X., Li, H., Shen, X., Liang, X., Wu, Y.: A modulation module for multi-task learning with applications in image retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 415\u2013432. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_25"},{"key":"55_CR39","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68790-8_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,18]],"date-time":"2022-12-18T14:17:34Z","timestamp":1671373054000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68790-8_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687892","9783030687908"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68790-8_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"23 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}