10 citations · 14 across the 8 of their papers we have counts for
9 papers
Robust Promptable Video Object Segmentation
Sohyun Lee, Yeho Gwon, Lukas Hoyer +3
The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in safety-critical domains. Thi…
De-occluding broadband metalens
Seungwoo Yoon, Dohyun Kang, Eunsue Choi +9
Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solutions to obstructions rely on a bulky c…
GaRA-SAM: Robustifying Segment Anything Model with Gated-Rank Adaptation
Sohyun Lee, Yeho Gwon, Lukas Hoyer +1
Improving robustness of the Segment Anything Model (SAM) to input degradations is critical for its deployment in high-stakes applications such as autonomous driving and robotics. O…
TestDG: Test-time Domain Generalization for Continual Test-time Adaptation
Sohyun Lee, Nayeong Kim, Juwon Kang +2
This paper studies continual test-time adaptation (CTTA), the task of adapting a model to constantly changing unseen domains in testing while preserving previously learned knowledg…
FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions
Sohyun Lee, Namyup Kim, Sungyeon Kim +1
Robust semantic segmentation under adverse conditions is crucial in real-world applications. To address this challenging task in practical scenarios where labeled normal condition…
Active Learning for Semantic Segmentation with Multi-class Label Query
Sehyun Hwang, Sohyun Lee, Hoyoung Kim +3
This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions…
Human Pose Estimation in Extremely Low-Light Conditions
Sohyun Lee, Jaesung Rim, Boseung Jeong +5
We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely…
Combating Label Distribution Shift for Active Domain Adaptation
Sehyun Hwang, Sohyun Lee, Sungyeon Kim +2
We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent a…
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation
Sohyun Lee, Taeyoung Son, Suha Kwak
Robust visual recognition under adverse weather conditions is of great importance in real-world applications. In this context, we propose a new method for learning semantic segment…