16 citations · 40 across the 9 of their papers we have counts for
9 papers
Ranked Entropy Minimization for Continual Test-Time Adaptation
Jisu Han, Jaemin Na, Wonjun Hwang
Test-time adaptation aims to adapt to realistic environments in an online manner by learning during test time. Entropy minimization has emerged as a principal strategy for test-tim…
OurDB: Ouroboric Domain Bridging for Multi-Target Domain Adaptive Semantic Segmentation
Seungbeom Woo, Geonwoo Baek, Taehoon Kim +3
Multi-target domain adaptation (MTDA) for semantic segmentation poses a significant challenge, as it involves multiple target domains with varying distributions. The goal of MTDA i…
Semantic Prompting with Image-Token for Continual Learning
Jisu Han, Jaemin Na, Wonjun Hwang
Continual learning aims to refine model parameters for new tasks while retaining knowledge from previous tasks. Recently, prompt-based learning has emerged to leverage pre-trained…
D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection
Dinh Phat Do, Taehoon Kim, Jaemin Na +4
Domain adaptation for object detection typically entails transferring knowledge from one visible domain to another visible domain. However, there are limited studies on adapting fr…
Switching Temporary Teachers for Semi-Supervised Semantic Segmentation
Jaemin Na, Jung-Woo Ha, Hyung Jin Chang +2
The teacher-student framework, prevalent in semi-supervised semantic segmentation, mainly employs the exponential moving average (EMA) to update a single teacher's weights based on…
SRIL: Selective Regularization for Class-Incremental Learning
Jisu Han, Jaemin Na, Wonjun Hwang
Human intelligence gradually accepts new information and accumulates knowledge throughout the lifespan. However, deep learning models suffer from a catastrophic forgetting phenomen…
Contrastive Vicinal Space for Unsupervised Domain Adaptation
Jaemin Na, Dongyoon Han, Hyung Jin Chang +1
Recent unsupervised domain adaptation methods have utilized vicinal space between the source and target domains. However, the equilibrium collapse of labels, a problem where the so…
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation
Jaemin Na, Heechul Jung, Hyung Jin Chang +1
Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adap…
Densely Guided Knowledge Distillation using Multiple Teacher Assistants
Wonchul Son, Jaemin Na, Junyong Choi +1
With the success of deep neural networks, knowledge distillation which guides the learning of a small student network from a large teacher network is being actively studied for mod…