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Title: Few-shot fine-grained classification with Spatial Attentive Comparison
Authors: Ruan, Xiaoqian
Lin, Guosheng
Long, Cheng
Lu, Shengli
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Ruan, X., Lin, G., Long, C. & Lu, S. (2021). Few-shot fine-grained classification with Spatial Attentive Comparison. Knowledge-Based Systems, 218, 106840-.
Project: RG28/18 (S)
RG22/19 (S)
Journal: Knowledge-Based Systems
Abstract: The 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.
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2021.106840
Schools: School of Computer Science and Engineering 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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