3 papers
cs.CV2026
DeCO: Discriminative Evidence Composition for Fine-Grained Dataset Distillation
Chuixuan Fan, Guang Li, Shijie Wang +5
Dataset distillation compresses a large training set into a compact synthetic set while preserving its downstream utility. However, existing methods primarily preserve global image…
cs.CV2026
FD: A Dedicated Framework for Fine-Grained Dataset Distillation
Hongxu Ma, Guang Li, Shijie Wang +5
Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoup…
cs.CV2025
Adversarial Reconstruction Feedback for Robust Fine-grained Generalization
Shijie Wang, Jian Shi, Haojie Li
Existing fine-grained image retrieval (FGIR) methods predominantly rely on supervision from predefined categories to learn discriminative representations for retrieving fine-graine…