3 papers
cs.CV2026
Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Shihao Hou, Chikai Shang, Zhiheng Yang +5
Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distributions. However, real-world…
cs.CV2026
Decision Boundary-aware Generation for Long-tailed Learning
Jiacheng Yang, Ruichi Zhang, Chikai Shang +5
Long-tailed data bias decision boundaries toward head classes and degrade tail class accuracy. Diffusion-based generative augmentation address this problem by generating additional…
cs.CV2026
CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning
Ruichi Zhang, Chikai Shang, Jiacheng Yang +4
Long-tailed distributions are common in real-world recognition tasks, where a few head classes have many samples while most tail classes have very few. Recently, fine-tuning founda…