Showing cs.CVShow all
2 papers · 1 filter
cs.CV2026
ReCalMatch:Reliability-Calibrated Semantic Guidance for Semi-Supervised Fine-Grained Recognition
Yundi Hong, Hongyang He, Zheng Fang +2
Semi-supervised fine-grained visual recognition is highly vulnerable to overconfident pseudo-label errors: visually similar categories frequently produce high-confidence yet incorr…
cs.CV2026
Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation
Xuanyu Liu, Zheng Fang, Hongyang He +2
Semi-supervised adaptation of vision foundation models (VFMs) commonly freezes the pretrained backbone and updates lightweight modules such as LoRA. However, pseudo-labels have mix…