9 citations · 15 across the 6 of their papers we have counts for
6 papers
Coarse-to-Fine Point Cloud Registration with SE(3)-Equivariant Representations
Cheng-Wei Lin, Tung-I Chen, Hsin-Ying Lee +2
Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose di…
CFVS: Coarse-to-Fine Visual Servoing for 6-DoF Object-Agnostic Peg-In-Hole Assembly
Bo-Siang Lu, Tung-I Chen, Hsin-Ying Lee +1
Robotic peg-in-hole assembly remains a challenging task due to its high accuracy demand. Previous work tends to simplify the problem by restricting the degree of freedom of the end…
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation
Tsung-Han Wu, Yi-Syuan Liou, Shao-Ji Yuan +4
In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with…
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data
Guan-Rong Lu, Yueh-Cheng Liu, Tung-I Chen +3
Anomaly awareness is an essential capability for safety-critical applications such as autonomous driving. While recent progress of robotics and computer vision has enabled anomaly…
ODIP: Towards Automatic Adaptation for Object Detection by Interactive Perception
Tung-I Chen, Jen-Wei Wang, Winston H. Hsu
Object detection plays a deep role in visual systems by identifying instances for downstream algorithms. In industrial scenarios, however, a slight change in manufacturing systems…
Dual-Awareness Attention for Few-Shot Object Detection
Tung-I Chen, Yueh-Cheng Liu, Hung-Ting Su +5
While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existin…