16 citations · 26 across the 7 of their papers we have counts for
7 papers · 1 filter
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…