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FOCUS: Frequency-Optimized Conditioning of DiffUSion Models for mitigating catastrophic forgetting during Test-Time Adaptation
Gabriel Tjio, Jie Zhang, Xulei Yang +6
Test-time adaptation enables models to adapt to evolving domains. However, balancing the tradeoff between preserving knowledge and adapting to domain shifts remains challenging for…
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation
Gabriel Tjio, Ping Liu, Yawei Luo +2
This work addresses the challenging domain adaptation setting in which knowledge from the labelled source domain dataset is available only from the pretrained black-box segmentatio…
Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation
Gabriel Tjio, Ping Liu, Chee-Keong Kwoh +1
Obtaining sufficient labeled data for training deep models is often challenging in real-life applications. To address this issue, we propose a novel solution for single-source doma…
Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
Gabriel Tjio, Ping Liu, Joey Tianyi Zhou +1
Convolutional neural networks typically perform poorly when the test (target domain) and training (source domain) data have significantly different distributions. While this proble…
Accurate Tumor Tissue Region Detection with Accelerated Deep Convolutional Neural Networks
Gabriel Tjio, Xulei Yang, Jia Mei Hong +4
Manual annotation of pathology slides for cancer diagnosis is laborious and repetitive. Therefore, much effort has been devoted to develop computer vision solutions. Our approach,…
Multi-Instance Multi-Scale CNN for Medical Image Classification
Shaohua Li, Yong Liu, Xiuchao Sui +4
Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs)…