243 citations · 318 across the 8 of their papers we have counts for
8 papers
4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič +52
The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy an…
CD-FKD: Cross-Domain Feature Knowledge Distillation for Robust Single-Domain Generalization in Object Detection
Junseok Lee, Sungho Shin, Seongju Lee +1
Single-domain generalization is essential for object detection, particularly when training models on a single source domain and evaluating them on unseen target domains. Domain shi…
ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation
Geonhyup Lee, Yeongjin Lee, Kangmin Kim +4
Contact-rich manipulation tasks such as precision assembly require precise control of interaction forces, yet existing imitation learning methods rely mainly on vision-only demonst…
3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results
Benjamin Kiefer, Lojze Žust, Jon Muhovič +43
The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive…
MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction
Seongju Lee, Junseok Lee, Yeonguk Yu +2
Multi-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. Learning-based approaches for multi-agent trajectory prediction, s…
Block Selection Method for Using Feature Norm in Out-of-distribution Detection
Yeonguk Yu, Sungho Shin, Seongju Lee +2
Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods commonly relied on the output of…
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
Seongju Lee, Yeonguk Yu, Seunghyeok Back +2
Automatic sleep scoring is essential for the diagnosis and treatment of sleep disorders and enables longitudinal sleep tracking in home environments. Conventionally, learning-based…
Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG
Hogeon Seo, Seunghyeok Back, Seongju Lee +3
A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring. To classify the sleep st…