5 accepted papers
Large Language Models (LLMs) hold significant potential for enhancing healthcare applications, yet their deployment is hindered by high computational and memory demands. Model compression techniques offer solutions to reduce these demands, but their impact on medical LLMs remains underexplored. In t
The scarcity data of medical field brings the collaborative training in medical vision-language pre-training (VLP) cross different clients. Therefore, the collaborative training in medical VLP faces two challenges: First, the medical data requires privacy, thus can not directly shared across differe…
3D object detection is one of the most important tasks in autonomous driving and robotics. Our research focuses on tackling low efficiency issue of point-based methods, and we propose a novel Instance-Centroid Faster Point Sampling (IC-FPS) framework. We design a Neighboring Feature Diffusion Module
Optimization and sampling based algorithms are two branches of methods in machine learning, while existing safe reinforcement learning (RL) algorithms are mainly based on optimization, it is still unclear whether sampling based methods can lead to desirable performance with safe policy. This paper f…
In the point cloud analysis task, the existing local feature aggregation descriptors (LFAD) do not fully utilize the neighborhood information of center points. Previous methods only use the distance information to constrain the local aggregation process, which is easy to be affected by abnormal poin…