{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:56:13Z","timestamp":1761897373967,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030585389"},{"type":"electronic","value":"9783030585396"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58539-6_38","type":"book-chapter","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T19:02:46Z","timestamp":1604689366000},"page":"631-646","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Collaboration by Competition: Self-coordinated Knowledge Amalgamation for Multi-talent Student Learning"],"prefix":"10.1007","author":[{"given":"Sihui","family":"Luo","sequence":"first","affiliation":[]},{"given":"Wenwen","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Xinchao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Dazhou","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Haihong","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Mingli","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,7]]},"reference":[{"key":"38_CR1","doi-asserted-by":"crossref","unstructured":"Achille, A., et al.: Task2vec: task embedding for meta-learning. In: IEEE International Conference on Computer Vision (ICCV), pp. 6430\u20136439 (2019)","DOI":"10.1109\/ICCV.2019.00653"},{"issue":"9","key":"38_CR2","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.1109\/TPAMI.2015.2500224","volume":"38","author":"H Azizpour","year":"2015","unstructured":"Azizpour, H., Razavian, A.S., Sullivan, J., Maki, A., Carlsson, S.: Factors of transferability for a generic convnet representation. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 38(9), 1790\u20131802 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"issue":"1\u20132","key":"38_CR3","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Mach. Learn. 79(1\u20132), 151\u2013175 (2010)","journal-title":"Mach. Learn."},{"key":"38_CR4","unstructured":"Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: Gradnorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: International Conference on Machine Learning (ICML), pp. 794\u2013803 (2018)"},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Dwivedi, K., Roig, G.: Representation similarity analysis for efficient task taxonomy & transfer learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12387\u201312396 (2019)","DOI":"10.1109\/CVPR.2019.01267"},{"key":"38_CR6","doi-asserted-by":"crossref","unstructured":"Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.304"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"38_CR8","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"38_CR9","unstructured":"Huang, Z., Wang, N.: Like what you like: Knowledge distill via neuron selectivity transfer. arXiv preprint arXiv:1707.01219 (2017)"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Liu, S., Johns, E., Davison, A.J.: End-to-end multi-task learning with attention. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1871\u20131880 (2019)","DOI":"10.1109\/CVPR.2019.00197"},{"key":"38_CR11","unstructured":"Liu, W., Rabinovich, A., Berg, A.C.: Parsenet: Looking wider to see better. arXiv preprint arXiv:1506.04579 (2015)"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Luo, S., Wang, X., Fang, G., Hu, Y., Tao, D., Song, M.: Knowledge amalgamation from heterogeneous networks by common feature learning. In: International Joint Conference on Artificial Intelligence (IJCAI) (2019)","DOI":"10.24963\/ijcai.2019\/428"},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3994\u20134003 (2016)","DOI":"10.1109\/CVPR.2016.433"},{"issue":"10","key":"38_CR14","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Ranjan, A., et al.: Competitive collaboration: joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12240\u201312249 (2019)","DOI":"10.1109\/CVPR.2019.01252"},{"key":"38_CR16","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: hints for thin deep nets. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"Ruder, S., Bingel, J., Augenstein, I., S\u00f8gaard, A.: Latent multi-task architecture learning. In: AAAI Conference on Artificial Intelligence (AAAI), vol. 33, pp. 4822\u20134829 (2019)","DOI":"10.1609\/aaai.v33i01.33014822"},{"key":"38_CR18","unstructured":"Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: Neural Information Processing Systems (NeurIPS), pp. 527\u2013538 (2018)"},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: CVPR Workshops, pp. 806\u2013813 (2014)","DOI":"10.1109\/CVPRW.2014.131"},{"key":"38_CR20","doi-asserted-by":"crossref","unstructured":"Shen, C., Wang, X., Song, J., Sun, L., Song, M.: Amalgamating knowledge towards comprehensive classification. In: AAAI Conference on Artificial Intelligence (AAAI) (2019)","DOI":"10.1609\/aaai.v33i01.33013068"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Shen, C., Xue, M., Wang, X., Song, J., Sun, L., Song, M.: Customizing student networks from heterogeneous teachers via adaptive knowledge amalgamation. In: IEEE International Conference on Computer Vision (ICCV), pp. 3504\u20133513 (2019)","DOI":"10.1109\/ICCV.2019.00360"},{"key":"38_CR22","unstructured":"Song, J., Chen, Y., Wang, X., Shen, C., Song, M.: Deep model transferability from attribution maps. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Song, J., et al.: Depara: deep attribution graph for deep knowledge transferability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00398"},{"key":"38_CR24","unstructured":"Standley, T., Zamir, A.R., Chen, D., Guibas, L., Malik, J., Savarese, S.: Which tasks should be learned together in multi-task learning? arXiv preprint arXiv:1905.07553 (2019)"},{"key":"38_CR25","doi-asserted-by":"crossref","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: IEEE International Conference on Computer Vision (ICCV), pp. 1365\u20131374 (2019)","DOI":"10.1109\/ICCV.2019.00145"},{"key":"38_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhao, H., Li, X., Tan, X.: Progressive blockwise knowledge distillation for neural network acceleration. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 2769\u20132775 (2018)","DOI":"10.24963\/ijcai.2018\/384"},{"key":"38_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/978-3-319-46484-8_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Z Wang","year":"2016","unstructured":"Wang, Z., Deng, Z., Wang, S.: Accelerating convolutional neural networks with dominant convolutional kernel and knowledge pre-regression. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 533\u2013548. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_32"},{"key":"38_CR28","doi-asserted-by":"crossref","unstructured":"Xu, D., Ouyang, W., Wang, X., Sebe, N.: PAD-Net: multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 675\u2013684 (2018)","DOI":"10.1109\/CVPR.2018.00077"},{"key":"38_CR29","doi-asserted-by":"crossref","unstructured":"Yang, Y., Qiu, J., Song, M., Tao, D., Wang, X.: Distilling knowledge from graph convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00710"},{"key":"38_CR30","doi-asserted-by":"crossref","unstructured":"Ye, J., Ji, Y., Wang, X., Gao, X., Song, M.: Data-free knowledge amalgamation via group-stack dual-GAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01253"},{"key":"38_CR31","doi-asserted-by":"crossref","unstructured":"Ye, J., Ji, Y., Wang, X., Ou, K., Tao, D., Song, M.: Student becoming the master: knowledge amalgamation for joint scene parsing, depth estimation, and more. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00294"},{"key":"38_CR32","doi-asserted-by":"crossref","unstructured":"Yu, X., Liu, T., Wang, X., Tao, D.: On compressing deep models by low rank and sparse decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.15"},{"key":"38_CR33","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"38_CR34","doi-asserted-by":"crossref","unstructured":"Zamir, A.R., Sax, A., Shen, W., Guibas, L.J., Malik, J., Savarese, S.: Taskonomy: disentangling task transfer learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3712\u20133722 (2018)","DOI":"10.1109\/CVPR.2018.00391"},{"key":"38_CR35","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Xu, R., Wang, X., Hou, P., Tang, H., Song, M.: Hearing lips: improving lip reading by distilling speech recognizers. In: AAAI Conference on Artificial Intelligence, (AAAI) (2020)","DOI":"10.1609\/aaai.v34i04.6174"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58539-6_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:21:48Z","timestamp":1730852508000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58539-6_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585389","9783030585396"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58539-6_38","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":"7 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}