{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:45:38Z","timestamp":1777657538045,"version":"3.51.4"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"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":["Machine Vision and Applications"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s00138-023-01486-z","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T03:02:28Z","timestamp":1702004548000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Few-shot object detection via data augmentation\u00a0and distribution calibration"],"prefix":"10.1007","volume":"35","author":[{"given":"Songhao","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,8]]},"reference":[{"key":"1486_CR1","unstructured":"Wang, X., Huang, T.E., Gonzalez, J., Darrell, T., Yu, F.: Frustratingly simple few-shot object detection. In: ICML, pp. 9919\u20139928 (2020)"},{"key":"1486_CR2","unstructured":"Xing, C., Rostamzadeh, N., Oreshkin, B.N., Pinheiro, P.O.: Adaptive cross-modal few-shot learning. In: NeurIPS, pp. 4848\u20134858 (2019)"},{"key":"1486_CR3","doi-asserted-by":"crossref","unstructured":"Wu, J., Liu, S., Huang, D., Wang, Y.: Multi-scale positive sample refinement for few-shot object detection. In: ECCV (16), pp. 456\u2013472 (2020)","DOI":"10.1007\/978-3-030-58517-4_27"},{"key":"1486_CR4","doi-asserted-by":"crossref","unstructured":"Sun, B., Li, B., Cai, S., Yuan, Y., Zhang, C.: FSCE: few-shot object detection via contrastive proposal encoding. In: CVPR, pp. 7352\u20137362 (2021)","DOI":"10.1109\/CVPR46437.2021.00727"},{"key":"1486_CR5","unstructured":"Kim, J., Yoon, I., Park, G.-M., Kim, J.-H.: Non-probabilistic cosine similarity loss for few-shot image classification. In: BMVC (2020)"},{"key":"1486_CR6","doi-asserted-by":"crossref","unstructured":"Karlinsky, L., Shtok, J., Harary, S., Schwartz, E., Aides, A., Feris, R.S., Giryes, R., Bronstein, A.M.: RepMet: Representative-based metric learning for classification and few-shot object detection. In: CVPR, pp. 5197\u20135206 (2019)","DOI":"10.1109\/CVPR.2019.00534"},{"key":"1486_CR7","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Marlet, R.: Few-shot object detection and viewpoint estimation for objects in the wild. In: ECCV, pp. 192\u2013210 (2020)","DOI":"10.1007\/978-3-030-58520-4_12"},{"key":"1486_CR8","doi-asserted-by":"crossref","unstructured":"Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., Darrell, T.: Few-shot object detection via feature reweighting. In: ICCV, pp. 8419\u20138428 (2019)","DOI":"10.1109\/ICCV.2019.00851"},{"key":"1486_CR9","doi-asserted-by":"crossref","unstructured":"Li, B., Yang, B., Liu, C., Liu, F., Ji, R., Ye, Q.: Beyond max-margin: class margin equilibrium for few-shot object detection. In: CVPR, pp. 7363\u20137372 (2021)","DOI":"10.1109\/CVPR46437.2021.00728"},{"key":"1486_CR10","unstructured":"Cao, Y., Wang, J., Jin, Y., Wu, T., Chen, K., Liu, Z., Lin, D.: Few-shot object detection via association and discrimination. In: NeurIPS, pp. 16570\u201316581 (2021)"},{"key":"1486_CR11","doi-asserted-by":"crossref","unstructured":"Han, G., Ma, J., Huang, S., Chen, L., Chang, S.-F.: Few-shot object detection with fully cross-transformer. In: CVPR, pp. 5311\u20135320 (2022)","DOI":"10.1109\/CVPR52688.2022.00525"},{"key":"1486_CR12","unstructured":"Zhang, X., Liu, F., Peng, Z., Guo, Z., Wan, F., Ji, X., Ye, Q.: Integral migrating pre-trained transformer encoder-decoders for visual object detection. In: CoRR (2022). https:\/\/arxiv.org\/abs\/2205.09613"},{"key":"1486_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.B.: Masked autoencoders are scalable vision learners. In: CVPR, pp. 15979\u201315988 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"1486_CR14","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NIPS, pp. 3630\u20133638 (2016)"},{"key":"1486_CR15","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"1486_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cai, Y., Lin, G., Shen, C.: DeepEMD: Few-shot image classification with differentiable earth mover's distance and structured classifiers. In: CVPR, pp. 12200\u201312210 (2020)","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"1486_CR17","doi-asserted-by":"crossref","unstructured":"Yang, B., Liu, C., Li, B., Jiao, J., Ye, Q.: Prototype mixture models for few-shot semantic segmentation. In: ECCV, pp. 763\u2013778 (2020)","DOI":"10.1007\/978-3-030-58598-3_45"},{"key":"1486_CR18","doi-asserted-by":"crossref","unstructured":"Liu, B., Ding, Y., Jiao, J., Ji, X., Ye, Q.: Anti-aliasing semantic reconstruction for few-shot semantic segmentation. In: CVPR, pp. 9747\u20139756 (2021)","DOI":"10.1109\/CVPR46437.2021.00962"},{"key":"1486_CR19","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2021.3058512","volume":"30","author":"B Liu","year":"2021","unstructured":"Liu, B., Jiao, J., Ye, Q.: Harmonic feature activation for few-shot semantic segmentation. IEEE Trans. Image Process. 30, 3142\u20133153 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"1486_CR20","doi-asserted-by":"crossref","unstructured":"Wang, Y.-X., Girshick, R.B., Hebert, M., Hariharan, B.: Low-shot learning from imaginary data. In: CVPR, pp. 7278\u20137286 (2018)","DOI":"10.1109\/CVPR.2018.00760"},{"key":"1486_CR21","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Girshick, R.B.: Low-Shot Visual Recognition by Shrinking and Hallucinating Features. ICCV 2017: 3037\u20133046.","DOI":"10.1109\/ICCV.2017.328"},{"key":"1486_CR22","unstructured":"Park, S.-J., Han, S., Baek, J.-W., Kim, I., Song, J., Lee, H., Han, J.-J., Ju Hwang, S.: Meta variance transfer: learning to augment from the others. In: ICML, pp. 7510\u20137520 (2020)"},{"key":"1486_CR23","unstructured":"Yang, S., Liu, L., Xu, M.: Free lunch for few-shot learning: distribution calibration. In: ICLR (2021)"},{"key":"1486_CR24","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV, pp. 213\u2013229 (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1486_CR25","doi-asserted-by":"crossref","unstructured":"Redmon, J., Kumar Divvala, S., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1486_CR26","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: ECCV, pp. 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1486_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Y.-X., Ramanan, D., Hebert, M.: Meta-learning to detect rare objects. In: ICCV, pp. 9924\u20139933 (2019)","DOI":"10.1109\/ICCV.2019.01002"},{"key":"1486_CR28","doi-asserted-by":"crossref","unstructured":"Li, A., Li, Z.: Transformation invariant few shot object detection. In: CVPR, pp. 3094\u20133102 (2021)","DOI":"10.1109\/CVPR46437.2021.00311"},{"key":"1486_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, C., Chen, F., Ahmed, U., Shen, Z., Savvides, M.: Semantic relation reasoning for shot-stable few-shot object detection. In: CVPR, pp. 8782\u20138791 (2021)","DOI":"10.1109\/CVPR46437.2021.00867"},{"key":"1486_CR30","doi-asserted-by":"crossref","unstructured":"Hu, H., Bai, S., Li, A., Cui, J., Wang, L.: Dense relation distillation with context-aware aggregation for few-shot object detection. In: CVPR, pp. 10185\u201310194 (2021)","DOI":"10.1109\/CVPR46437.2021.01005"},{"key":"1486_CR31","doi-asserted-by":"crossref","unstructured":"Fan, Z., Ma, Y., Li, Z., Sun, J.: Generalized few-shot object detection without forgetting. In: CVPR, pp. 4527\u20134536 (2021)","DOI":"10.1109\/CVPR46437.2021.00450"},{"key":"1486_CR32","doi-asserted-by":"crossref","unstructured":"Qiao, L., Zhao, Y., Li, Z., Qiu, X., Wu, J., Zhang, C.: DeFRCN: decoupled faster R-CNN for few-shot object detection. In: ICCV, pp. 8661\u20138670 (2021)","DOI":"10.1109\/ICCV48922.2021.00856"},{"key":"1486_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, S.,Wang, L., Murray, N., Koniusz, P.: Kernelized few-shot object detection with efficient integral aggregation. In: CVPR, pp. 19185\u201319194 (2022)","DOI":"10.1109\/CVPR52688.2022.01861"},{"key":"1486_CR34","doi-asserted-by":"crossref","unstructured":"Kaul, P., Xie, W., Zisserman, A.: Label, verify, correct: a simple few shot object detection method. In: CVPR, pp. 14217\u201314227 (2022)","DOI":"10.1109\/CVPR52688.2022.01384"},{"key":"1486_CR35","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T.-Y., Cubuk, E.D., Le, Q.V., Zoph, B.: Simple copy-paste is a strong data augmentation method for instance segmentation. In: CVPR, pp. 2918\u20132928 (2021)","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"1486_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wang, Y.-X.: Hallucination improves few-shot object detection. In: CVPR, pp. 13008\u201313017 (2021)","DOI":"10.1109\/CVPR46437.2021.01281"},{"key":"1486_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Z., Yan, S., He, X., Sun, J.: Distribution alignment: a unified framework for long-tail visual recognition. In: CVPR, 2361\u20132370 (2021)","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"1486_CR38","unstructured":"Song, H., Diethe, T., Kull, M., Flach, P.A.: Distribution calibration for regression. In: ICML, pp. 5897\u20135906 (2019)"},{"key":"1486_CR39","doi-asserted-by":"crossref","unstructured":"Shen, Z., Liu, Z., Qin, J., Huang, L., Cheng, K.-T., Savvides, M.: S2-BNN: bridging the gap between self-supervised real and 1-bit neural networks via guided distribution calibration. In: CVPR, pp. 2165\u20132174 (2021)","DOI":"10.1109\/CVPR46437.2021.00220"},{"key":"1486_CR40","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.J.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"1486_CR41","unstructured":"Zhang, W., Wang, Y.-X., Forsyth, DA.: Cooperating RPN's improve few-shot object detection. In: CoRR (2020). https:\/\/arxiv.org\/abs\/2112.02814"},{"key":"1486_CR42","doi-asserted-by":"crossref","unstructured":"Guo, H., Pasunuru, R., Bansal, M.: Multi-source domain adaptation for text classification via distanceNet-bandits. In: AAAI, pp. 7830\u20137838 (2020)","DOI":"10.1609\/aaai.v34i05.6288"},{"key":"1486_CR43","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zheng, L., Guan, T., Yu, J., Yang, Y.: Taking a closer look at domain shift: category-level adversaries for semantics consistent domain adaptation. In: CVPR, pp. 2507\u20132516 (2019)","DOI":"10.1109\/CVPR.2019.00261"},{"key":"1486_CR44","doi-asserted-by":"crossref","unstructured":"Dvornik, N., Mairal, J., Schmid, C.: Modeling visual context is key to augmenting object detection datasets. In: ECCV, pp. 375\u2013391 (2018)","DOI":"10.1007\/978-3-030-01258-8_23"},{"key":"1486_CR45","doi-asserted-by":"crossref","unstructured":"Fang, H., Sun, J., Wang, R., Gou, M., Li, Y.-L., Lu, C.: InstaBoost: boosting instance segmentation via probability map guided copy-pasting. In: ICCV, pp. 682\u2013691 (2019)","DOI":"10.1109\/ICCV.2019.00077"},{"key":"1486_CR46","unstructured":"Shangguan, Z., Rostami, M.: Improved region proposal network for enhanced few-shot object detection. In: CoRR (2023). https:\/\/arxiv.org\/abs\/2308.07535"},{"key":"1486_CR47","doi-asserted-by":"crossref","unstructured":"Li, J., Zhang, Y., Qiang, W., Si, L., Jiao, C., Hu, X., Zheng, C., Sun, F.: Disentangle and remerge: interventional knowledge distillation for few-shot object detection from a conditional causal perspective. In: AAAI, pp. 1323\u20131333 (2023)","DOI":"10.1609\/aaai.v37i1.25216"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01486-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01486-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01486-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T09:06:55Z","timestamp":1706000815000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01486-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,8]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["1486"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01486-z","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,8]]},"assertion":[{"value":"16 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"11"}}