AI RESEARCH
Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
arXiv CS.CV
•
ArXi:2604.14958v1 Announce Type: new Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-based methods typically rely solely on spatial domain features. Confined to this single perspective, models inevitably suffer from inherent texture biases, entangling essential structural details with high-frequency background noise.