6 citations · 16 across the 4 of their papers we have counts for
6 papers
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning
Tianhao Wu, Fangwei Zhong, Yiran Geng +4
Grasping moving objects, such as goods on a belt or living animals, is an important but challenging task in robotics. Conventional approaches rely on a set of manually defined obje…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area
Zheng Yuan, Tianhao Wu, Qinwen Wang +3
Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicl…
A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles
Zhe Wang, Xinhang Li, Tianhao Wu +2
Federated Deep Learning (FDL) is helping to realize distributed machine learning in the Internet of Vehicles (IoV). However, FDL's global model needs multiple clients to upload lea…
Light Field Image Super-Resolution Using Deformable Convolution
Yingqian Wang, Jungang Yang, Longguang Wang +4
Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce beneficial angular information for image super-resolution (SR). However, it is challenging…
DeOccNet: Learning to See Through Foreground Occlusions in Light Fields
Yingqian Wang, Tianhao Wu, Jungang Yang +3
Background objects occluded in some views of a light field (LF) camera can be seen by other views. Consequently, occluded surfaces are possible to be reconstructed from LF images.…