2 papers
cs.CV2025
RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels
Nan Xiang, Lifeng Xing, Dequan Jin
In few-shot learning (FSL), the labeled samples are scarce. Thus, label errors can significantly reduce classification accuracy. Since label errors are inevitable in realistic lear…
cs.CV2025
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery
Jiayi Su, Dequan Jin
Generalized category discovery (GCD) is a highly popular task in open-world recognition, aiming to identify unknown class samples using known class data. By leveraging pre-training…