Yeonchool Park — accepted AI-conference papers

2024

ADNet: Non-Local Affinity Distillation Network for Lightweight Depth Completion With Guidance From Missing LiDAR Points

Janghyun Kim, Jeonghyun Noh, Mingyu Jeong, Wonju Lee, Yeonchool Park, Jinsun Park

RA-L 2024

Depth completion is one of the crucial methods to estimate dense depth information of surrounding environments for various real-world applications such as autonomous driving, robotics, and augmented reality. In these real-world applications, it is strictly required for a depth completion model to ac

Cited by 9 Source ↗Scholar ↗

2022

VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation

Su Ho Han, Sukjun Hwang, Seoung Wug Oh, Yeonchool Park, Hyunwoo Kim, Min-Jung Kim +1

CVPR 2022oral

For online video instance segmentation (VIS), fully utilizing the information from previous frames in an efficient manner is essential for real-time applications. Most previous methods follow a two-stage approach requiring additional computations such as RPN and RoIAlign, and do not fully exploit th…

Cited by 42 PDF ↗code ↗Scholar ↗