13 citations · 20 across the 6 of their papers we have counts for
6 papers
Brain-inspired analogical mixture prototypes for few-shot class-incremental learning
Wanyi Li, Wei Wei, Yongkang Luo +1
Few-shot class-incremental learning (FSCIL) poses significant challenges for artificial neural networks due to the need to efficiently learn from limited data while retaining knowl…
Progressive Transfer Learning for Dexterous In-Hand Manipulation with Multi-Fingered Anthropomorphic Hand
Yongkang Luo, Wanyi Li, Peng Wang +3
Dexterous in-hand manipulation for a multi-fingered anthropomorphic hand is extremely difficult because of the high-dimensional state and action spaces, rich contact patterns betwe…
DVGG: Deep Variational Grasp Generation for Dextrous Manipulation
Wei Wei, Daheng Li, Peng Wang +4
Grasping with anthropomorphic robotic hands involves much more hand-object interactions compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the stu…
GPR: Grasp Pose Refinement Network for Cluttered Scenes
Wei Wei, Yongkang Luo, Fuyu Li +4
Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an eff…
Deep Domain Adaptive Object Detection: a Survey
Wanyi Li, Fuyu Li, Yongkang Luo +2
Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test…
Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images
Peng Liu, Fuyu Li, Wanyi Li
Object detection in thermal images is an important computer vision task and has many applications such as unmanned vehicles, robotics, surveillance and night vision. Deep learning…