AI RESEARCH
Adaptive receptive field-based spatial-frequency feature reconstruction network for few-shot fine-grained image classification
arXiv CS.CV
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ArXi:2604.16936v1 Announce Type: new Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature-based FSFGIC methods is how to choose the size of the receptive field to extract feature descriptors (including spatial and frequency feature descriptors) from different category input images, thereby better performing the FSFGIC tasks. To address this, an adaptive receptive field-based spatial-frequency feature reconstruction network (ARF-SFR-Net) is proposed.