9 citations · 15 across the 4 of their papers we have counts for
4 papers
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
Chak Fong Chong, Jielong Guo, Xu Yang +2
Multi-label image classification datasets are often partially labeled where many labels are missing, posing a significant challenge to training accurate deep classifiers. However,…
Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement
Xinyi Fang, Xu Yang, Chak Fong Chong +4
Accurate classification of laryngeal vascular as benign or malignant is crucial for early detection of laryngeal cancer. However, organizations with limited access to laryngeal vas…
Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labels
Chak Fong Chong, Xinyi Fang, Jielong Guo +4
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image. Assigning pseudo-labels to unknown labels to gain additional t…
Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification
Chak Fong Chong, Yapeng Wang, Benjamin Ng +2
Classification on smartphone-captured chest X-ray (CXR) photos to detect pathologies is challenging due to the projective transformation caused by the non-ideal camera position. Re…