Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144270
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dc.contributor.authorZhang, Chien_US
dc.contributor.authorCai, Yujunen_US
dc.contributor.authorLin, Guoshengen_US
dc.contributor.authorShen, Chunhuaen_US
dc.date.accessioned2020-10-26T06:04:12Z-
dc.date.available2020-10-26T06:04:12Z-
dc.date.issued2020-
dc.identifier.citationZhang, C., Cai, Y., Lin, G., & Shen, C. (2020). DeepEMD : few-shot image classification with differentiable Earth Mover’s Distance and structured classifiers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12203-12213.en_US
dc.identifier.urihttps://hdl.handle.net/10356/144270-
dc.description.abstractIn this paper, we address the few-shot classification task from a new perspective of optimal matching between image regions. We adopt the Earth Mover’s Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance. The EMD generates the optimal matching flows between structural elements that have the minimum matching cost, which is used to represent the image distance for classification. To generate the important weights of elements in the EMD formulation, we design a cross-reference mechanism, which can effectively minimize the impact caused by the cluttered background and large intra-class appearance variations. To handle k-shot classification, we propose to learn a structured fully connected layer that can directly classify dense image representations with the EMD. Based on the implicit function theorem, the EMD can be inserted as a layer into the network for end-to-end training. We conduct comprehensive experiments to validate our algorithm and we set new state-of-the-art performance on four popular few-shot classification benchmarks, namely miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100) and Caltech-UCSD Birds-200-2011 (CUB).en_US
dc.description.sponsorshipAI Singaporeen_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationAISG-RP-2018-003en_US
dc.relationRG126/17 (S)en_US
dc.relationRG28/18 (S)en_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDeepEMD : few-shot image classification with differentiable Earth Mover’s Distance and structured classifiersen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020en_US
dc.description.versionAccepted versionen_US
dc.identifier.spage12203en_US
dc.identifier.epage12213en_US
dc.subject.keywordsDeep Neural Networksen_US
dc.subject.keywordsEarth Mover’s Distance (EMD)en_US
dc.description.acknowledgementThis research is supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-003) and the MOE Tier-1 research grants: RG126/17 (S) and RG28/18 (S). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.en_US
dc.description.acknowledgementThis research is supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-003) and the MOE Tier-1 research grants: RG126/17 (S) and RG28/18 (S). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.en_US
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