{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:55:51Z","timestamp":1743112551198,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":46,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784868"},{"type":"electronic","value":"9789819784875"}],"license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"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":[[2025]]},"DOI":"10.1007\/978-981-97-8487-5_24","type":"book-chapter","created":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T07:04:35Z","timestamp":1730617475000},"page":"338-352","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Making the Primary Task Primary: Boosting Few-Shot Classification by Gradient-Biased Multi-task Learning"],"prefix":"10.1007","author":[{"given":"Yunchen","family":"Wu","sequence":"first","affiliation":[]},{"given":"Boyao","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Huo","sequence":"additional","affiliation":[]},{"given":"Wenbin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yunhao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Tinghao","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"An, Y., Xue, H., Zhao, X., Zhang, L.: Conditional self-supervised learning for few-shot classification. In: IJCAI (2021)","DOI":"10.24963\/ijcai.2021\/295"},{"key":"24_CR2","unstructured":"Bertinetto, L., Henriques, J.F., Torr, P.H.S., Vedaldi, A.: Meta-learning with differentiable closed-form solvers. In: ICLR (2019)"},{"key":"24_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":"24_CR4","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Bursuc, A., Komodakis, N., P\u00e9rez, P., Cord, M.: Boosting few-shot visual learning with self-supervision. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00815"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"key":"24_CR7","unstructured":"Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: ICLR (2018)"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Guo, Y., Cheung, N.M.: Attentive weights generation for few shot learning via information maximization. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01351"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Hu, S.X., Li, D., St\u00fchmer, J., Kim, M., Hospedales, T.M.: Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00886"},{"key":"24_CR11","unstructured":"Javaloy, A., Valera, I.: Rotograd: Dynamic gradient homogenization for multi-task learning. In: ICLR (2022)"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Kang, D., Cho, M.: Integrative few-shot learning for classification and segmentation. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00974"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, H., Kim, G.: Model-agnostic boundary-adversarial sampling for test-time generalization in few-shot learning. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58452-8_35"},{"key":"24_CR14","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML (2015)"},{"key":"24_CR15","unstructured":"Laenen, S., Bertinetto, L.: On episodes, prototypical networks, and few-shot learning. In: NeurIPS (2021)"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Larsson, G., Maire, M., Shakhnarovich, G.: Learning representations for automatic colorization. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46493-0_35"},{"key":"24_CR17","unstructured":"Lee, H., Hwang, S.J., Shin, J.: Rethinking data augmentation: Self-supervision and self-distillation. In: CVPR (2019)"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01091"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Li, H., Eigen, D., Dodge, S., Zeiler, M., Wang, X.: Finding task-relevant features for few-shot learning by category traversal. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00009"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Li, W., Wang, L., Xu, J., Huo, J., Gao, Y., Luo, J.: Revisiting local descriptor based image-to-class measure for few-shot learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00743"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Lin, H., Han, G., Ma, J., Huang, S., Lin, X., Chang, S.F.: Supervised masked knowledge distillation for few-shot transformers. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01882"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Liu, C., Fu, Y., Xu, C., Yang, S., Li, J., Wang, C., Zhang, L.: Learning a few-shot embedding model with contrastive learning. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i10.17047"},{"key":"24_CR23","unstructured":"Liu, L., Li, Y., Kuang, Z., Xue, J., Chen, Y., Yang, W., Liao, Q., Zhang, W.: Towards impartial multi-task learning. In: ICLR (2021)"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Mangla, P., Singh, M., Sinha, A., Kumari, N., Balasubramanian, V.N., Krishnamurthy, B.: Charting the right manifold: Manifold mixup for few-shot learning. In: WACV (2020)","DOI":"10.1109\/WACV45572.2020.9093338"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"24_CR26","unstructured":"Oreshkin, B.N., L\u00f3pez, P.R., Lacoste, A.: TADAM: task dependent adaptive metric for improved few-shot learning. In: NeurIPS (2018)"},{"key":"24_CR27","unstructured":"Rajasegaran, J., Khan, S.H., Hayat, M., Khan, F.S., Shah, M.: Self-supervised knowledge distillation for few-shot learning. In: BMVC (2020)"},{"key":"24_CR28","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: ICLR (2017)"},{"key":"24_CR29","unstructured":"Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J.B., Larochelle, H., Zemel, R.S.: Meta-learning for semi-supervised few-shot classification. In: ICLR (2018)"},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. (2014)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"24_CR31","unstructured":"Rusu, A.A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., Hadsell, R.: Meta-learning with latent embedding optimization. In: ICLR (2019)"},{"key":"24_CR32","unstructured":"Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: NeurIPS (2018)"},{"key":"24_CR33","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: NeurIPS (2017)"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Su, J.C., Maji, S., Hariharan, B.: When does self-supervision improve few-shot learning? In: ECCV (2020)","DOI":"10.1007\/978-3-030-58571-6_38"},{"key":"24_CR35","doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, Y., Chua, T.S., Schiele, B.: Meta-transfer learning for few-shot learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00049"},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J.B., Isola, P.: Rethinking few-shot image classification: a good embedding is all you need? In: ECCV (2020)","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"24_CR38","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NeurIPS (2016)"},{"key":"24_CR39","unstructured":"Wang, Y., Chao, W., Weinberger, K.Q., van\u00a0der Maaten, L.: Simpleshot: Revisiting nearest-neighbor classification for few-shot learning. CoRR (2019)"},{"key":"24_CR40","doi-asserted-by":"crossref","unstructured":"Wertheimer, D., Tang, L., Hariharan, B.: Few-shot classification with feature map reconstruction networks. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00792"},{"key":"24_CR41","doi-asserted-by":"crossref","unstructured":"Ye, H., Hu, H., Zhan, D., Sha, F.: Few-shot learning via embedding adaptation with set-to-set functions. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"24_CR42","unstructured":"Ye, H.J., Ming, L., chuan Zhan, D., Chao, W.L.: Few-shot learning with a strong teacher. IEEE Trans. Pattern Anal. Mach. Intell. (2021)"},{"key":"24_CR43","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. In: NeurIPS (2020)"},{"key":"24_CR44","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: BMVC (2016)","DOI":"10.5244\/C.30.87"},{"key":"24_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cai, Y., Lin, G., Shen, C.: Deepemd: Few-shot image classification with differentiable earth mover\u2019s distance and structured classifiers. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"24_CR46","unstructured":"Zhang, M., Zhang, J., Lu, Z., Xiang, T., Ding, M., Huang, S.: Iept: Instance-level and episode-level pretext tasks for few-shot learning. In: ICLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8487-5_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T07:08:40Z","timestamp":1730617720000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8487-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"ISBN":["9789819784868","9789819784875"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8487-5_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"4 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}