4 accepted papers
Online object detection and tracking are crucial for embodied intelligence systems, including autonomous vehicles and robotics. Traditional approaches employ a pipeline structure to perform detection and tracking separately, which can not fully leverage information from the detector. Moreover, most
Batch normalization (BN) is widely recognized as an essential method in training deep neural networks, facilitating convergence and enhancing model stability. However, in Federated Learning (FL) contexts, where training data are typically heterogeneous and clients often face resource constraints, th…
Randomized Controlled Trials (RCTs) are rigorous clinical studies crucial for reliable decision-making, but their credibility can be compromised by bias. The Cochrane Risk of Bias tool (RoB 2) assesses this risk, yet manual assessments are time-consuming and labor-intensive. Previous approaches have…
This study addresses the challenge of multi-robot cooperative exploration under limited local observations in environments with dynamic robot populations. To achieve efficient area coverage within constrained timeframes, we propose the Multi-Robot Informative Planner (MIP), a novel reinforcement lea