Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study

ArXi:2605.15599v1 Announce Type: cross Extreme low-data fine-grained classification is common in expert domains where labeling is expensive, yet practitioners still need principled guidance for selecting pretrained encoders. We study emerald inclusion grading with a custom dataset of labeled images across three classes and ask: under matched backbone capacity, how does pre