{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:48:02Z","timestamp":1743032882059,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031783944"},{"type":"electronic","value":"9783031783951"}],"license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"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\/978-3-031-78395-1_7","type":"book-chapter","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T09:37:29Z","timestamp":1733132249000},"page":"97-113","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A-FSL: Adaptive Few-Shot Learning via Task-Driven Context Aggregation and Attentive Feature Refinement"],"prefix":"10.1007","author":[{"given":"Riti","family":"Paul","sequence":"first","affiliation":[]},{"given":"Sahil","family":"Vora","sequence":"additional","affiliation":[]},{"given":"Nupur","family":"Thakur","sequence":"additional","affiliation":[]},{"given":"Baoxin","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"7_CR1","unstructured":"Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M.W., Pfau, D., Schaul, T., Shillingford, B., De\u00a0Freitas, N.: Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems 29 (2016)"},{"key":"7_CR2","unstructured":"Bertinetto, L., Henriques, J.F., Torr, P.H., Vedaldi, A.: Meta-learning with differentiable closed-form solvers. arXiv preprint arXiv:1805.08136 (2018)"},{"issue":"9","key":"7_CR3","doi-asserted-by":"publisher","first-page":"4594","DOI":"10.1109\/TIP.2019.2910052","volume":"28","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Fu, Y., Zhang, Y., Jiang, Y.G., Xue, X., Sigal, L.: Multi-level semantic feature augmentation for one-shot learning. IEEE Trans. Image Process. 28(9), 4594\u20134605 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, H., Yang, S., Zhou, J.T., Guo, L., Wen, B.: Frequency guidance matters in few-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 11814\u201311824 (2023)","DOI":"10.1109\/ICCV51070.2023.01085"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Fei, N., Gao, Y., Lu, Z., Xiang, T.: Z-score normalization, hubness, and few-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 142\u2013151 (2021)","DOI":"10.1109\/ICCV48922.2021.00021"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4367\u20134375 (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Guo, Y., Cheung, N.M.: Attentive weights generation for few shot learning via information maximization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 13499\u201313508 (2020)","DOI":"10.1109\/CVPR42600.2020.01351"},{"key":"7_CR8","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs\/1512.03385 (2015), http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"7_CR9","unstructured":"Hou, R., Chang, H., Ma, B., Shan, S., Chen, X.: Cross attention network for few-shot classification. Advances in Neural Information Processing Systems 32 (2019)"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition. pp. 831\u2013839 (2019)","DOI":"10.1109\/CVPR.2019.00092"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Kan, B., Wang, T., Lu, W., Zhen, X., Guan, W., Zheng, F.: Knowledge-aware prompt tuning for generalizable vision-language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 15670\u201315680 (2023)","DOI":"10.1109\/ICCV51070.2023.01436"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Khattak, M.U., Rasheed, H., Maaz, M., Khan, S., Khan, F.S.: Maple: Multi-modal prompt learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 19113\u201319122 (2023)","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"7_CR13","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"issue":"1","key":"7_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF02289565","volume":"29","author":"JB Kruskal","year":"1964","unstructured":"Kruskal, J.B.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1\u201327 (1964)","journal-title":"Psychometrika"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 10657\u201310665 (2019)","DOI":"10.1109\/CVPR.2019.01091"},{"key":"7_CR16","doi-asserted-by":"publisher","unstructured":"Liu, B., Cao, Y., Lin, Y., Li, Q., Zhang, Z., Long, M., Hu, H.: Negative Margin Matters: Understanding Margin in Few-Shot Classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 438\u2013455. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_26","DOI":"10.1007\/978-3-030-58548-8_26"},{"key":"7_CR17","unstructured":"Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141 (2017)"},{"key":"7_CR18","unstructured":"Nickel, M., Kiela, D.: Poincar\u00e9 embeddings for learning hierarchical representations. Advances in neural information processing systems 30 (2017)"},{"key":"7_CR19","unstructured":"Oreshkin, B., Rodr\u00edguez\u00a0L\u00f3pez, P., Lacoste, A.: Tadam: Task dependent adaptive metric for improved few-shot learning. Advances in neural information processing systems 31 (2018)"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Peng, Z., Li, Z., Zhang, J., Li, Y., Qi, G.J., Tang, J.: Few-shot image recognition with knowledge transfer. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 441\u2013449 (2019)","DOI":"10.1109\/ICCV.2019.00053"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Qi, H., Brown, M., Lowe, D.G.: Low-shot learning with imprinted weights. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5822\u20135830 (2018)","DOI":"10.1109\/CVPR.2018.00610"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7229\u20137238 (2018)","DOI":"10.1109\/CVPR.2018.00755"},{"key":"7_CR23","unstructured":"Raghu, A., Raghu, M., Bengio, S., Vinyals, O.: Rapid learning or feature reuse? towards understanding the effectiveness of maml. arXiv preprint arXiv:1909.09157 (2019)"},{"key":"7_CR24","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. arXiv preprint arXiv:1803.00676 (2018)"},{"key":"7_CR25","unstructured":"Rusu, A.A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., Hadsell, R.: Meta-learning with latent embedding optimization. arXiv preprint arXiv:1807.05960 (2018)"},{"key":"7_CR26","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International conference on machine learning. pp. 1842\u20131850. PMLR (2016)"},{"key":"7_CR27","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"7_CR28","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Advances in neural information processing systems 30 (2017)"},{"key":"7_CR29","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: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp.\u00a01\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"7_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-030-58568-6_16","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Tian","year":"2020","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: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 266\u2013282. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_16"},{"key":"7_CR32","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et\u00a0al.: Matching networks for one shot learning. Advances in neural information processing systems 29 (2016)"},{"key":"7_CR33","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)"},{"key":"7_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1007\/978-3-319-46466-4_37","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y-X Wang","year":"2016","unstructured":"Wang, Y.-X., Hebert, M.: Learning to Learn: Model Regression Networks for Easy Small Sample Learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 616\u2013634. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_37"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhang, T., Zhang, Y., Wu, F.: Task-aware part mining network for few-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 8433\u20138442 (2021)","DOI":"10.1109\/ICCV48922.2021.00832"},{"key":"7_CR36","doi-asserted-by":"crossref","unstructured":"Xie, J., Long, F., Lv, J., Wang, Q., Li, P.: Joint distribution matters: Deep brownian distance covariance for few-shot classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 7972\u20137981 (2022)","DOI":"10.1109\/CVPR52688.2022.00781"},{"key":"7_CR37","unstructured":"Xing, C., Rostamzadeh, N., Oreshkin, B., O\u00a0Pinheiro, P.O.: Adaptive cross-modal few-shot learning. Advances in Neural Information Processing Systems 32 (2019)"},{"key":"7_CR38","doi-asserted-by":"crossref","unstructured":"Xu, C., Fu, Y., Liu, C., Wang, C., Li, J., Huang, F., Zhang, L., Xue, X.: Learning dynamic alignment via meta-filter for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 5182\u20135191 (2021)","DOI":"10.1109\/CVPR46437.2021.00514"},{"key":"7_CR39","doi-asserted-by":"crossref","unstructured":"Yang, F., Wang, R., Chen, X.: Sega: semantic guided attention on visual prototype for few-shot learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 1056\u20131066 (2022)","DOI":"10.1109\/WACV51458.2022.00165"},{"key":"7_CR40","doi-asserted-by":"crossref","unstructured":"Yang, F., Wang, R., Chen, X.: Semantic guided latent parts embedding for few-shot learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 5447\u20135457 (January 2023)","DOI":"10.1109\/WACV56688.2023.00541"},{"key":"7_CR41","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: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 12203\u201312213 (2020)","DOI":"10.1109\/CVPR42600.2020.01222"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78395-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T17:40:14Z","timestamp":1741974014000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78395-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"ISBN":["9783031783944","9783031783951"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78395-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"3 December 2024","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":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}