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dc.contributor.authorRuan, Xiaoqianen_US
dc.contributor.authorLin, Guoshengen_US
dc.contributor.authorLong, Chengen_US
dc.contributor.authorLu, Shenglien_US
dc.identifier.citationRuan, X., Lin, G., Long, C. & Lu, S. (2021). Few-shot fine-grained classification with Spatial Attentive Comparison. Knowledge-Based Systems, 218, 106840-.
dc.description.abstractThe main goal of this paper is to propose a novel model, named Spatial Attentive Comparison Network (SACN), which is used to address a problem, termed few-shot fine-grained recognition (FSFG). FSFG is to recognize fine-grained examples with only a few samples, which is challenging for deep neural networks. SACN is made up of three modules, namely feature extraction module, selective-comparison similarity module (SCSM), and classification module: feature extraction module extracts the distinctive information into feature maps, SCSM is used to fuse the features of support set with those of the query set based on selective comparison. Considering the noisy background and tiny differences between different categories, we apply SCSM to fuse these features by arranging different weights pixel by pixel, and all these weights are learned automatically. Moreover, we apply pyramid structure to enrich the features. By conducting comprehensive experiments on three fine-grained datasets, namely CUB-200-2011 (CUB Birds), Stanford Dogs Dataset, and Stanford Cars Dataset, we demonstrate that the proposed method achieves superior performance over the competing baselines.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.relationRG28/18 (S)en_US
dc.relationRG22/19 (S)en_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.rights© 2021 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleFew-shot fine-grained classification with Spatial Attentive Comparisonen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.subject.keywordsFew-Shot Learningen_US
dc.subject.keywordsFine-Grained Classificationen_US
dc.description.acknowledgementThis work is partly supported by MOE Tier-1 Singapore research grants: RG28/18 (S) and RG22/19 (S).en_US
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