7 citations · 7 across the 7 of their papers we have counts for
7 papers
Cautious Context Steering for Language Model Personalization
Gihoon Kim, Jeyoung Lee, Suhan Woo +4
Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds. Existing methods typically train a separa…
BridgeTA: Bridging the Representation Gap in Knowledge Distillation via Teacher Assistant for Bird's Eye View Map Segmentation
Beomjun Kim, Suhan Woo, Sejong Heo +1
Bird's-Eye-View (BEV) map segmentation is one of the most important and challenging tasks in autonomous driving. Camera-only approaches have drawn attention as cost-effective alter…
Environmental Change Detection for Real-World Change Analysis
Kyusik Cho, Suhan Woo, Hongje Seong +1
Scene Change Detection (SCD) evaluates changes using predefined query-reference (i.e., present-past) image pairs. However, this formulation overlooks a critical dependency: the cor…
ALC: Active and Automated Label Correction for Semantic Segmentation
Youjin Jeon, Kyusik Cho, Suhan Woo +1
Active Label Correction (ALC) has emerged as a promising solution to the high cost and error-prone nature of manual pixel-wise annotation in semantic segmentation, by actively iden…
HypeVPR: Exploring Hyperbolic Space for Perspective to Equirectangular Visual Place Recognition
Suhan Woo, Seongwon Lee, Jinwoo Jang +1
Visual environments are inherently hierarchical, as a panoramic view naturally encompasses and organizes multiple perspective views within its field. Capturing this hierarchy is cr…
Decomposition of Neural Discrete Representations for Large-Scale 3D Mapping
Minseong Park, Suhan Woo, Euntai Kim
Learning efficient representations of local features is a key challenge in feature volume-based 3D neural mapping, especially in large-scale environments. In this paper, we introdu…
3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns forroad scene interpretation
A. Hernández, S. Woo, H. Corrales +4
Road detection and segmentation is a crucial task in computer vision for safe autonomous driving. With this in mind, a new net architecture (3D-DEEP) and its end-to-end training me…