|Table of Contents|

Few-Shot Image Classification Based on Self-Supervised and Adaptive-Aware Relation Network(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年04期
Page:
68-78
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Few-Shot Image Classification Based on Self-Supervised and Adaptive-Aware Relation Network
Author(s):
Dai Xinjie1; 2; Zheng Jiajie1; 2; Yuan Yuanfei1; 2; Wang Lijin1; 2; Wu Qingshou3
(1.College of Computer and Information Sciences,Fujian Agriculture and Forestry University,Fuzhou 350002,China)
(2.Key Laboratory of Smart Agricultrue and Foresty in Fujian Province University,Fujian Agriculture and Forestry University,Fuzhou 350002,China)
(3.School of Mathematics and Computer Science,Wuyi University,Wuyishan 354300,China)
Keywords:
few-shot classification; self-supervised learning; adaptive-aware relation network; metric learning; dual correlated attention mechanism; dynamic weight averaging
PACS:
O643; X703
DOI:
10.3969/j.issn.1672-1292.2024.04.007
Abstract:
Relation networks,as a method for few-shot classification through metric analysis of sample similarities,are limited by their inherent local connectivity which restricts the utilization of global features of samples. Furthermore,these networks demonstrate insufficient generalization ability when data is scarce. This paper proposes a hybrid method of few-shot classification combining self-supervised learning with adaptive perception relation networks. Firstly,it enhances model feature representation and generalization ability by integrating self-supervised instance-level and scene-level auxiliary tasks,supervised few-shot classification auxiliary tasks,and adaptive dual-relation attention tasks. Additionally,a dynamic weight averaging strategy is introduced to adaptively optimize weights between auxiliary tasks. Instance-level auxiliary tasks focus on learning transfer knowledge of unknown categories in rotated samples,scene-level tasks ensure consistency in classifier predictions across different rotated datasets,while few-shot classification auxiliary tasks average supervised predictions on expanded datasets,optimizing classification efficacy. The adaptive perception relation network tasks automatically adjust image feature variations through an adaptive layer,and enhance inter-feature interactions via a dual-relation attention mechanism,thereby promoting key feature recognition. The proposed method has been validated on the miniImageNet,tieredImageNet and CUB-200-2011 datasets,demonstrating its capability to significantly enhance the classification performance of relation networks across various backbone networks,proving the feasibility and effectiveness of the proposed approach.

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Last Update: 2024-12-15