3 accepted papers
Dexterous grasp generation is a predominant task that enables robots to perform human-level manipulation. However, a dexterous hand always maintains high-dimensional DoF and actuation space, making existing approaches that rely on holistic latent representations difficult to produce high-quality and
The rapid advancement of generative models, which produce increasingly realistic synthetic images, urgently demands robust and generalizable detection methods. Consequently, research has largely pivoted to leveraging large-scale Vision Foundation Models (VFMs) for enhanced generalization. However, e
Low-Rank Adaptation (LoRA) has become increasingly popular for efficiently fine-tuning large language models (LLMs) with minimal resources. However, traditional methods that serve multiple LoRA models independently result in redundant computation and low GPU utilization. This paper addresses these i…