1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraints
Jay Bear, Adam Prügel-Bennett, Jonathon Hare
Iterative algorithms solve problems by taking steps until a solution is reached. Models in the form of Deep Thinking (DT) networks have been demonstrated to learn iterative algorit…
cs.CV2024
Revisiting Cross-Domain Problem for LiDAR-based 3D Object Detection
Ruixiao Zhang, Juheon Lee, Xiaohao Cai +1
Deep learning models such as convolutional neural networks and transformers have been widely applied to solve 3D object detection problems in the domain of autonomous driving. Whil…
cs.CV2024
Detect Closer Surfaces that can be Seen: New Modeling and Evaluation in Cross-domain 3D Object Detection
Ruixiao Zhang, Yihong Wu, Juheon Lee +2
The performance of domain adaptation technologies has not yet reached an ideal level in the current 3D object detection field for autonomous driving, which is mainly due to signifi…