{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:52:13Z","timestamp":1743094333840,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031262838"},{"type":"electronic","value":"9783031262845"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26284-5_25","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T08:02:59Z","timestamp":1677052979000},"page":"409-424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gestalt-Guided Image Understanding for\u00a0Few-Shot Learning"],"prefix":"10.1007","author":[{"given":"Kun","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiansheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huimin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"25_CR1","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18\u201324 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 8748\u20138763 (2021)"},{"issue":"4","key":"25_CR2","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","volume":"28","author":"L Fei-Fei","year":"2006","unstructured":"Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594\u2013611 (2006). https:\/\/doi.org\/10.1109\/TPAMI.2006.79","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR3","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems: Annual Conference on Neural Information Processing Systems 2016, 5\u201310 December 2016, Barcelona, Spain, vol. 29, pp. 3630\u20133638 (2016)"},{"key":"25_CR4","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems: Annual Conference on Neural Information Processing Systems 2017, 30 December 2017, Long Beach, CA, USA, vol. 30, pp. 4077\u20134087 (2017)"},{"key":"25_CR5","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017. Proceedings of Machine Learning Research, vol. 70, pp. 1126\u20131135 (2017)"},{"key":"25_CR6","doi-asserted-by":"publisher","unstructured":"Chen, Y., Liu, Z., Xu, H., Darrell, T., Wang, X.: Meta-baseline: exploring simple meta-learning for few-shot learning. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, 10\u201317 October 2021, pp. 9042\u20139051 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00893","DOI":"10.1109\/ICCV48922.2021.00893"},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Zhang, C., Cai, Y., Lin, G., Shen, C.: DeepEMD: few-shot image classification with differentiable earth mover\u2019s distance and structured classifiers. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 12200\u201312210 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01222","DOI":"10.1109\/CVPR42600.2020.01222"},{"issue":"9","key":"25_CR8","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149\u20135169 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Thrun, S., Pratt, L.: Learning to learn: introduction and overview. In: Thrun, S., Pratt, L. (eds.) Learning to Learn, pp. 3\u201317. Springer, Cham (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5529-2_1","DOI":"10.1007\/978-1-4615-5529-2_1"},{"key":"25_CR10","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R., et al.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2, Lille (2015)"},{"key":"25_CR11","unstructured":"Satorras, V.G., Estrach, J.B.: Few-shot learning with graph neural networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April\u20133 May 2018, Conference Track Proceedings (2018)"},{"key":"25_CR12","first-page":"13073","volume":"34","author":"X Luo","year":"2021","unstructured":"Luo, X., et al.: Rectifying the shortcut learning of background for few-shot learning. Adv. Neural. Inf. Process. Syst. 34, 13073\u201313085 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR13","unstructured":"Yang, S., Liu, L., Xu, M.: Free lunch for few-shot learning: distribution calibration. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021 (2021)"},{"key":"25_CR14","doi-asserted-by":"publisher","unstructured":"Tang, X., Teng, Z., Zhang, B., Fan, J.: Self-supervised network evolution for few-shot classification. In: Zhou, Z. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event\/Montreal, Canada, 19\u201327 August 2021, pp. 3045\u20133051 (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/419","DOI":"10.24963\/ijcai.2021\/419"},{"key":"25_CR15","doi-asserted-by":"publisher","unstructured":"An, Y., Xue, H., Zhao, X., Zhang, L.: Conditional self-supervised learning for few-shot classification. In: Zhou, Z. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event\/Montreal, Canada, 19\u201327 August 2021, pp. 2140\u20132146 (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/295","DOI":"10.24963\/ijcai.2021\/295"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, Z., Hu, X.: Learning intact features by erasing-inpainting for few-shot classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8401\u20138409 (2021)","DOI":"10.1609\/aaai.v35i9.17021"},{"key":"25_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-58558-7_2","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Afrasiyabi","year":"2020","unstructured":"Afrasiyabi, A., Lalonde, J.-F., Gagn\u00e9, C.: Associative alignment for few-shot image classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 18\u201335. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_2"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Wagemans, J., et al.: A century of gestalt psychology in visual perception: II. conceptual and theoretical foundations. Psychol. Bull. 138(6), 1218 (2012)","DOI":"10.1037\/a0029334"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Henle, M.: The selected papers of Wolfgang K\u00f6hler. Philos. Phenomenol. Res. 33(2), 270\u2013271 (1972)","DOI":"10.2307\/2106468"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Hamlyn, D.W.: The Psychology of Perception: A Philosophical Examination of Gestalt Theory and Derivative Theories of Perception. Routledge, New York (2017)","DOI":"10.4324\/9781315473291"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Brennan, J.F., Houde, K.A.: History and Systems of Psychology. Cambridge University Press, Cambridge (2017)","DOI":"10.1017\/9781316827178"},{"key":"25_CR22","unstructured":"Stevenson, H.: Emergence: The Gestalt Approach to Change. Unleashing Executive and Organizational Potential (2012). Retrieved July 2012"},{"key":"25_CR23","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 dataset (2011)"},{"issue":"3","key":"25_CR24","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"25_CR25","unstructured":"Li, W., et al.: LibFewShot: a comprehensive library for few-shot learning. arXiv preprint arXiv:2109.04898 (2021)"},{"key":"25_CR26","doi-asserted-by":"publisher","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 4367\u20134375 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00459","DOI":"10.1109\/CVPR.2018.00459"},{"key":"25_CR27","doi-asserted-by":"publisher","unstructured":"Luo, X., Chen, Y., Wen, L., Pan, L., Xu, Z.: Boosting few-shot classification with view-learnable contrastive learning. In: 2021 IEEE International Conference on Multimedia and Expo, ICME 2021, Shenzhen, China, July 2021, pp. 1\u20136 (2021). https:\/\/doi.org\/10.1109\/ICME51207.2021.9428444","DOI":"10.1109\/ICME51207.2021.9428444"},{"key":"25_CR28","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Chu, W.H., Li, Y.J., Chang, J.C., Wang, Y.C.F.: Spot and learn: a maximum-entropy patch sampler for few-shot image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6251\u20136260 (2019)","DOI":"10.1109\/CVPR.2019.00641"},{"key":"25_CR30","doi-asserted-by":"publisher","unstructured":"Li, A., Huang, W., Lan, X., Feng, J., Li, Z., Wang, L.: Boosting few-shot learning with adaptive margin loss. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 12573\u201312581 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01259","DOI":"10.1109\/CVPR42600.2020.01259"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., Liu, H.: Nico++: towards better benchmarking for domain generalization. arXiv preprint arXiv:2204.08040 (2022)","DOI":"10.1109\/CVPR52729.2023.01539"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26284-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T11:17:11Z","timestamp":1701947831000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26284-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031262838","9783031262845"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26284-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","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":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","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":"CMT Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","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":"277","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":"33% - 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":"3.3","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":"2.6","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":"For the ACCV 2022 workshops 25 papers have been accepted from 40 submissions","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)"}}]}}