243 citations · 330 across the 21 of their papers we have counts for
21 papers
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…
3D Flow Diffusion Policy: Visuomotor Policy Learning via Generating Flow in 3D Space
Sangjun Noh, Dongwoo Nam, Kangmin Kim +4
Learning robust visuomotor policies that generalize across diverse objects and interaction dynamics remains a central challenge in robotic manipulation. Most existing approaches re…
BiGraspFormer: End-to-End Bimanual Grasp Transformer
Kangmin Kim, Seunghyeok Back, Geonhyup Lee +3
Bimanual grasping is essential for robots to handle large and complex objects. However, existing methods either focus solely on single-arm grasping or employ separate grasp generat…
GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes
Seunghyeok Back, Joosoon Lee, Kangmin Kim +8
Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on si…
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise
Yeonguk Yu, Minhwan Ko, Sungho Shin +2
Deep neural networks have demonstrated remarkable performance in various vision tasks, but their success heavily depends on the quality of the training data. Noisy labels are a cri…
GraspSAM: When Segment Anything Model Meets Grasp Detection
Sangjun Noh, Jongwon Kim, Dongwoo Nam +3
Grasp detection requires flexibility to handle objects of various shapes without relying on prior knowledge of the object, while also offering intuitive, user-guided control. This…
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…
Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation
Yeonguk Yu, Sungho Shin, Seunghyeok Back +3
Test-time adaptation (TTA) aims to adapt a pre-trained model to a new test domain without access to source data after deployment. Existing approaches typically rely on self-trainin…
PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation
Geonhyup Lee, Joosoon Lee, Sangjun Noh +3
The study addresses the foundational and challenging task of peg-in-hole assembly in robotics, where misalignments caused by sensor inaccuracies and mechanical errors often result…
High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinement
Seunghyeok Back, Sangbeom Lee, Kangmin Kim +4
Accurate and efficient segmentation of unknown objects in unstructured environments is essential for robotic manipulation. Unknown Object Instance Segmentation (UOIS), which aims t…
Enhancing Low-resolution Face Recognition with Feature Similarity Knowledge Distillation
Sungho Shin, Yeonguk Yu, Kyoobin Lee
In this study, we introduce a feature knowledge distillation framework to improve low-resolution (LR) face recognition performance using knowledge obtained from high-resolution (HR…
Learning to Place Unseen Objects Stably using a Large-scale Simulation
Sangjun Noh, Raeyoung Kang, Taewon Kim +3
Object placement is a fundamental task for robots, yet it remains challenging for partially observed objects. Existing methods for object placement have limitations, such as the re…
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…
Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition
Sungho Shin, Joosoon Lee, Junseok Lee +2
Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention si…
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…
Automatic Detection of Injection and Press Mold Parts on 2D Drawing Using Deep Neural Network
Junseok Lee, Jongwon Kim, Jumi Park +3
This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network. We developed a deep learning pipeline…
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
Seunghyeok Back, Joosoon Lee, Taewon Kim +4
Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment. Although previous works achieved encouraging results, they were limi…
Deep Learning based Food Instance Segmentation using Synthetic Data
D. Park, J. Lee, K. Lee
In the process of intelligently segmenting foods in images using deep neural networks for diet management, data collection and labeling for network training are very important but…
Multiple Classification with Split Learning
Jongwon Kim, Sungho Shin, Yeonguk Yu +2
Privacy issues were raised in the process of training deep learning in medical, mobility, and other fields. To solve this problem, we present privacy-preserving distributed deep le…
Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data
Seunghyeok Back, Jongwon Kim, Raeyoung Kang +2
Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered an…
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…