3 citations · 10 across the 12 of their papers we have counts for
12 papers
Mitigating the Bias in the Model for Continual Test-Time Adaptation
Inseop Chung, Kyomin Hwang, Jayeon Yoo +1
Continual Test-Time Adaptation (CTA) is a challenging task that aims to adapt a source pre-trained model to continually changing target domains. In the CTA setting, a model does no…
ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering
Seunghyeon Seo, Yeonjin Chang, Jayeon Yoo +3
Recent advancements in the Neural Radiance Field (NeRF) have enhanced its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practi…
What, How, and When Should Object Detectors Update in Continually Changing Test Domains?
Jayeon Yoo, Dongkwan Lee, Inseop Chung +2
It is a well-known fact that the performance of deep learning models deteriorates when they encounter a distribution shift at test time. Test-time adaptation (TTA) algorithms have…
ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open-Vocabulary Object Detection
Joonhyun Jeong, Geondo Park, Jayeon Yoo +2
Open-vocabulary object detection (OVOD) aims to recognize novel objects whose categories are not included in the training set. In order to classify these unseen classes during trai…
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning
JunHoo Lee, Jayeon Yoo, Nojun Kwak
In this paper, we hypothesize that gradient-based meta-learning (GBML) implicitly suppresses the Hessian along the optimization trajectory in the inner loop. Based on this hypothes…
Non-invasive Liver Fibrosis Screening on CT Images using Radiomics
Jay J. Yoo, Khashayar Namdar, Sean Carey +4
Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver. Methods: For this retrospective, single-centre study, radiom…
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images
Jay J. Yoo, Khashayar Namdar, Matthias W. Wagner +5
Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging. Using machine learning for this problem generally requires manuall…
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images
Jay J. Yoo, Khashayar Namdar, Farzad Khalvati
Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annota…
Unsupervised Domain Adaptation for One-stage Object Detector using Offsets to Bounding Box
Jayeon Yoo, Inseop Chung, Nojun Kwak
Most existing domain adaptive object detection methods exploit adversarial feature alignment to adapt the model to a new domain. Recent advances in adversarial feature alignment st…
Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation for Semantic Segmentation
Inseop Chung, Jayeon Yoo, Nojun Kwak
"Self-training" has become a dominant method for semantic segmentation via unsupervised domain adaptation (UDA). It creates a set of pseudo labels for the target domain to give exp…
Dynamic Collective Intelligence Learning: Finding Efficient Sparse Model via Refined Gradients for Pruned Weights
Jangho Kim, Jayeon Yoo, Yeji Song +2
With the growth of deep neural networks (DNN), the number of DNN parameters has drastically increased. This makes DNN models hard to be deployed on resource-limited embedded system…
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
Hyojin Park, Jayeon Yoo, Seohyeong Jeong +2
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the curre…