The paper introduces the first formulation of convex Q-learning for Mark...
Neural Radiance Fields (NeRFs) have achieved great success in the past f...
In recent years, there has been a significant increase in focus on the
i...
Semantically coherent out-of-distribution (SCOOD) detection aims to disc...
In recent years there has been a collective research effort to find new
...
Convex Q-learning is a recent approach to reinforcement learning, motiva...
User preference modeling is a vital yet challenging problem in personali...
Point cloud registration is a fundamental problem in 3D computer vision....
Weakly-supervised temporal action localization aims to localize action
i...
LiDAR point cloud streams are usually sparse in time dimension, which is...
Predicting the future can significantly improve the safety of intelligen...
Online action detection is a task with the aim of identifying ongoing ac...
Keypoint detector and descriptor are two main components of point cloud
...
Slot filling and intent detection are two main tasks in spoken language
...
We propose a deep reinforcement learning (DRL) methodology for the track...
The I4U consortium was established to facilitate a joint entry to NIST
s...
Detecting vehicles with strong robustness and high efficiency has become...