5 accepted papers
Multi-Agent Debate (MAD) is an emerging paradigm that leverages the reasoning abilities of Large Language Models (LLMs) by encouraging them to collaboratively solve problems through human-like discussions. However, current MAD methods typically constrain agents to follow fixed discussion pipelines,
CAD drawing restoration is one of the most urgent needs in industrial manufacturing. The existing research focuses on the digitization of CAD drawings, However, there are actually many problems in digitized CAD drawings due to the upgrading of engineering drafting software, and it is difficult to re…
Recent advancements in autonomous driving have seen a paradigm shift towards end-to-end learning paradigms, which map sensory inputs directly to driving actions, thereby enhancing the robustness and adaptability of autonomous vehicles. However, these models often sacrifice interpretability, posing s
The lack of interpretability in the field of medical image analysis has significant ethical and legal implications. Existing interpretable methods in this domain encounter several challenges, including dependency on specific models, difficulties in understanding and visualization, and issues related…
We propose LIO-Vehicle, a new tightly-coupled vehicle dynamics extension of LiDAR inertial odometry (LIO) method that provides highly accurate, robust, and real-time vehicle trajectory estimation. Since most existing LiDAR-based localization methods are not specifically proposed for vehicles, they d