4 accepted papers
This paper presents a novel end-to-end trajectory planning framework that integrates LiDAR-based perception with trajectory optimization, enabling safe and efficient navigation in dynamic environments without relying on semantic detection or explicit kinematic modeling. Learning-based dynamic collis…
This paper presents a graph-based multi-agent reinforcement learning framework for scalable UAV formation control and target tracking. The framework introduces a conflict-aware graph representation that aggregates neighborhood information through attention-based message passing, enabling each UAV to…
Color correction (CC) plays a pivotal role in camera imaging. Existing approaches usually conduct CC tuning by minimizing ∆E (e.g. ∆E2000), a standard metric proposed by CIE for representing color differences in LAB space. However, we observe that not all the colors with identical ∆E error to the ta…
Optimizing Deep Neural Networks (DNNs) to obtain high-quality models for efficient real-world deployment has posed multi-faceted challenges to machine learning engineers. Existing methods either search for neural architectures in heuristic design spaces or apply low-level adjustments to computation…