2 papers
cs.CV2026
SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning
Hezhao Liu, Jiacheng Yang, Junlong Gao +4
In open-world semi-supervised learning (OWSSL), a model learns from labeled data and unlabeled data containing both known and novel classes. In practical OWSSL applications, models…
cs.CV2025
FATE: A Prompt-Tuning-Based Semi-Supervised Learning Framework for Extremely Limited Labeled Data
Hezhao Liu, Yang Lu, Mengke Li +4
Semi-supervised learning (SSL) has achieved significant progress by leveraging both labeled data and unlabeled data. Existing SSL methods overlook a common real-world scenario when…