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