4 citations · 7 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…