🔗 Causal Inference¶

🎞️ ECCV2026 · 1 paper notes

FD\(^2\): A Dedicated Framework for Fine-Grained Dataset Distillation: Addressing the issue that existing decoupled dataset distillation methods ignore discriminative local regions on fine-grained datasets, FD\(^2\) introduces Counterfactual Attention Learning (CAL) to extract discriminative attention maps and class prototypes. Integrating fine-grained feature constraints and intra-class sample similarity constraints during the distillation phase, it serves as a plug-and-play module that significantly improves the distillation quality of SRe2L++ and FADRM+ on fine-grained datasets such as CUB-200-2011, FGVC-Aircraft, and Stanford Cars, achieving a maximum improvement of +15.1% at IPC=1.