{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:40:03Z","timestamp":1755862803584,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,2,2]]},"DOI":"10.1145\/3651671.3651701","type":"proceedings-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T18:55:50Z","timestamp":1717786550000},"page":"424-432","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SRCPT: Spatial Reconstruction Contrastive Pretext Task for Improving Few-Shot Image Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1802-4344","authenticated-orcid":false,"given":"Zhenbang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0912-0928","authenticated-orcid":false,"given":"Pengfei","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4867-6811","authenticated-orcid":false,"given":"Yi","family":"Rong","sequence":"additional","affiliation":[{"name":"Sanya Science and Education Innovation Park of Wuhan University of Technology, China and School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"[1] LeCun Y.; Bengio Y.; and Hinton G. 2015. Deep learning. nature 521(7553): 436\u2013444.","key":"e_1_3_2_1_1_1","DOI":"10.1038\/nature14539"},{"volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14225\u201314234","author":"Meng Q.","unstructured":"[2] Meng, Q.; Zhao, S.; Huang, Z.; and Zhou, F. 2021. MagFace: A Universal Representation for Face Recognition and Quality Assessment. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14225\u201314234.","key":"e_1_3_2_1_2_1"},{"key":"e_1_3_2_1_3_1","volume-title":"ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems","volume":"25","author":"Krizhevsky A.","unstructured":"[3] Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1109\/TPAMI.2006.79"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1126\/science.aab3050"},{"unstructured":"[6] Koch G.; Zemel R.; Salakhutdinov R.; et al. 2015. Siamese neural networks for one-shot image recognition. In ICML deep learning workshop volume 2.","key":"e_1_3_2_1_6_1"},{"key":"e_1_3_2_1_7_1","volume-title":"k","author":"Vinyals O.","year":"2016","unstructured":"[7] Vinyals, O.; Blundell, C.; Lillicrap, T.; kavukcuoglu, k.; and Wierstra, D. 2016. Matching Networks for One Shot Learning. In Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc."},{"key":"e_1_3_2_1_8_1","volume-title":"Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems","volume":"30","author":"Snell J.","unstructured":"[8] Snell, J.; Swersky, K.; and Zemel, R. 2017. Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc."},{"unstructured":"[9] Finn C.; Abbeel P.; and Levine S. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning volume 70 of Proceedings of Machine Learning Research 1126\u20131135. PMLR.","key":"e_1_3_2_1_9_1"},{"volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 8059\u20138068","author":"Gidaris S.","unstructured":"[10] Gidaris, S.; Bursuc, A.; Komodakis, N.; Perez, P.; and Cord, M. 2019. Boosting Few-Shot Visual Learning With Self Supervision. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 8059\u20138068.","key":"e_1_3_2_1_10_1"},{"volume-title":"Self-supervised Knowledge Distillation for Few Shot Learning. In British Machine Vision Conference.","author":"Rajasegaran J.","unstructured":"[11] Rajasegaran, J.; Khan, S.; Hayat, M.; Khan, F. S.; and Shah, M. 2021. Self-supervised Knowledge Distillation for Few Shot Learning. In British Machine Vision Conference.","key":"e_1_3_2_1_11_1"},{"unstructured":"[12] Lee H.; Hwang S. J.; and Shin J. 2020. Self-supervised Label Augmentation via Input Transformations. In Proceedings of the 37th International Conference on Machine Learning volume 119 of Proceedings of Machine Learning Research 5714\u20135724. PMLR.","key":"e_1_3_2_1_12_1"},{"doi-asserted-by":"crossref","unstructured":"[13] Su J.-C.; Maji S.; and Hariharan B. 2020. When Does Self-supervision Improve Few-Shot Learning? In Computer Vision \u2013 ECCV 2020 645\u2013666. Springer International Publishing.","key":"e_1_3_2_1_13_1","DOI":"10.1007\/978-3-030-58571-6_38"},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Liu C.","unstructured":"[14] Liu, C.; Fu, Y.; Xu, C.; Yang, S.; Li, J.; Wang, C.; and Zhang, L. 2021. Learning a Few-shot Embedding Model with Contrastive Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, 8635\u20138643."},{"volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 8845\u20138855","author":"Islam A.","unstructured":"[15] Islam, A.; Chen, C.-F. R.; Panda, R.; Karlinsky, L.; Radke, R.; and Feris, R. 2021. A Broad Study on the Transferability of Visual Representations With Contrastive Learning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 8845\u20138855.","key":"e_1_3_2_1_15_1"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1199\u20131208","author":"Sung F.","unstructured":"[16] Sung, F.; Yang, Y.; Zhang, L.; Xiang, T.; Torr, P. H.; and Hospedales, T. M. 2018. Learning to Compare: Relation Network for Few-Shot Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1199\u20131208.","key":"e_1_3_2_1_16_1"},{"key":"e_1_3_2_1_17_1","first-page":"8649","volume-title":"AAAI","author":"Wenbin Li","year":"2019","unstructured":"[17] Wenbin Li, Jinglin Xu, Jing Huo, Lei Wang, Yang Gao, and Jiebo Luo, \u201cDistribution consistency based covariance metric networks for few-shot learning,\u201d in AAAI, 2019, pp. 8642\u20138649. 1, 2, 3"},{"key":"e_1_3_2_1_18_1","first-page":"7268","volume-title":"CVPR","author":"Wenbin Li","year":"2019","unstructured":"[18] Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, and Jiebo Luo, \u201cRevisiting local descriptor based image-to-class measure for few-shot learning,\u201d in CVPR, 2019, pp. 7260\u20137268. 1, 2, 3, 5"},{"key":"e_1_3_2_1_19_1","volume-title":"Multi-scale adaptive task attention network for few-shot learning","author":"Haoxing Chen","year":"2011","unstructured":"[19] Haoxing Chen, Huaxiong Li, Yaohui Li, and Chunlin Chen, \u201cMulti-scale adaptive task attention network for few-shot learning,\u201d arXiv preprint arXiv:2011.14479, 2020. 1, 3"},{"volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11719\u201311727","author":"Jamal M. A.","unstructured":"[20] Jamal, M. A.; and Qi, G.-J. 2019. Task Agnostic Meta Learning for Few-Shot Learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11719\u201311727.","key":"e_1_3_2_1_20_1"},{"volume-title":"International Conference on Learning Representations.","author":"Ravi S.","unstructured":"[21] Ravi, S.; and Larochelle, H. 2017. Optimization as a Model for Few-Shot Learning. In International Conference on Learning Representations.","key":"e_1_3_2_1_21_1"},{"unstructured":"[22] Li Z.; Zhou F.; Chen F.; and Li H. 2017. Meta-SGD: Learning to Learn Quickly for Few Shot Learning. CoRR abs\/1707.09835.","key":"e_1_3_2_1_22_1"},{"volume-title":"British Machine Vision Conference.","author":"Rajasegaran J.","unstructured":"[23] Rajasegaran, J.; Khan, S.; Hayat, M.; Khan, F. S.; and Shah, M. 2021. Self-supervised Knowledge Distillation for Few shot Learning. In British Machine Vision Conference.","key":"e_1_3_2_1_23_1"},{"doi-asserted-by":"crossref","unstructured":"[24] Su J.-C.; Maji S.; and Hariharan B. 2020. When Does Self-supervision Improve Few-Shot Learning? In Computer Vision \u2013 ECCV 2020 645\u2013666. Springer International Publishing.","key":"e_1_3_2_1_24_1","DOI":"10.1007\/978-3-030-58571-6_38"},{"volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 8059\u20138068","author":"Gidaris S.","unstructured":"[25] Gidaris, S.; Bursuc, A.; Komodakis, N.; Perez, P.; and Cord, M. 2019. Boosting Few-Shot Visual Learning With Self Supervision. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 8059\u20138068.","key":"e_1_3_2_1_25_1"},{"volume-title":"Spatial Contrastive Learning for Few-Shot Classification. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 671\u2013686","author":"Ouali Y.","unstructured":"[26] Ouali, Y.; Hudelot, C.; and Tami, M. 2021. Spatial Contrastive Learning for Few-Shot Classification. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 671\u2013686. Springer International Publishing.","key":"e_1_3_2_1_26_1"},{"volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8012\u20138021","author":"Wertheimer D.","unstructured":"[27] Wertheimer, D.; Tang, L.; and Hariharan, B. 2021. Few-Shot Classification With Feature Map Reconstruction Networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8012\u20138021.","key":"e_1_3_2_1_27_1"},{"key":"e_1_3_2_1_28_1","volume-title":"A new meta-baseline for few-shot learning","author":"Yinbo Chen","year":"2020","unstructured":"[28] Yinbo Chen, Xiaolong Wang, Zhuang Liu, Huijuan Xu, and Trevor Darrell. A new meta-baseline for few-shot learning, 2020."},{"key":"e_1_3_2_1_29_1","first-page":"4375","volume-title":"2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018","author":"Spyros Gidaris","year":"2018","unstructured":"[29] Spyros Gidaris and Nikos Komodakis. Dynamic few-shot visual learning without forgetting. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 4367\u20134375, 2018."},{"key":"e_1_3_2_1_30_1","first-page":"8814","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020","author":"Han-Jia Ye","year":"2020","unstructured":"[30] Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha. Few Shot learning via embedding adaptation with set-to-set functions. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 8805\u20138814, 2020."},{"key":"e_1_3_2_1_31_1","volume-title":"Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016","author":"Oriol Vinyals","year":"2016","unstructured":"[31] Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. Matching networks for one shot learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 3630\u20133638, 2016. 2, 6, 7"},{"key":"e_1_3_2_1_32_1","volume-title":"6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings","author":"Mengye Ren","year":"2018","unstructured":"[32] Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel. Meta-learning for semi-supervised few-shot classification. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, 2018. 2, 7, 12"},{"unstructured":"[33] C. Wah S. Branson P. Welinder P. Perona and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001 California Institute of Technology 2011. 2 6","key":"e_1_3_2_1_33_1"},{"volume-title":"International Conference on Learning Representations.","author":"Chen W.-Y.","unstructured":"[34] Chen, W.-Y.; Liu, Y.-C.; Kira, Z.; Wang, Y.-C. F.; and Huang, J.-B. 2019. A Closer Look at Few-shot Classification. In International Conference on Learning Representations.","key":"e_1_3_2_1_34_1"},{"key":"e_1_3_2_1_35_1","first-page":"2227","volume-title":"The IEEE Winter Conference on Applications of Computer Vision","author":"Puneet Mangla","year":"2020","unstructured":"[35] Puneet Mangla, Nupur Kumari, Abhishek Sinha, Mayank Singh, Balaji Krishnamurthy, and Vineeth N Balasubramanian. Charting the right manifold: Manifold mixup for few shot learning. In The IEEE Winter Conference on Applications of Computer Vision, pages 2218\u20132227, 2020. 6, 7, 16"},{"volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8808\u20138817","author":"Ye H.-J.","unstructured":"[36] Ye, H.-J.; Hu, H.; Zhan, D.-C.; and Sha, F. 2020. Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8808\u20138817.","key":"e_1_3_2_1_36_1"}],"event":{"acronym":"ICMLC 2024","name":"ICMLC 2024: 2024 16th International Conference on Machine Learning and Computing","location":"Shenzhen China"},"container-title":["Proceedings of the 2024 16th International Conference on Machine Learning and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3651671.3651701","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3651671.3651701","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:21:38Z","timestamp":1755861698000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3651671.3651701"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,2]]},"references-count":36,"alternative-id":["10.1145\/3651671.3651701","10.1145\/3651671"],"URL":"https:\/\/doi.org\/10.1145\/3651671.3651701","relation":{},"subject":[],"published":{"date-parts":[[2024,2,2]]},"assertion":[{"value":"2024-06-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}