Papers 3D Parameter-Efficient Fine-Tuning for Classification
“3D Parameter-Efficient Fine-Tuning for Classification” 태그가 달린 논문 4편 · 필터 해제
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
Recently, leveraging pre-training techniques to enhance point cloud models has become a hot research topic. However, existing approaches typically require full fine-tuning of pre-trained models to achieve satisfied perfo…
3D Parameter-Efficient Fine-Tuning for Classification3D Point Cloud ClassificationGeneral Knowledgeparameter-efficient fine-tuning+1Positional Prompt Tuning for Efficient 3D Representation Learning
Point cloud analysis has achieved significant development and is well-performed in multiple downstream tasks like point cloud classification and segmentation, etc. Being conscious of the simplicity of the position encodi…
3D Parameter-Efficient Fine-Tuning for Classification3D Point Cloud Classificationparameter-efficient fine-tuningPoint Cloud Classification+2Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
Point cloud analysis has achieved outstanding performance by transferring point cloud pre-trained models. However, existing methods for model adaptation usually update all model parameters, i.e., full fine-tuning paradig…
3D Parameter-Efficient Fine-Tuning for ClassificationGPUTransfer LearningInstance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
Pre-trained point cloud models have found extensive applications in 3D understanding tasks like object classification and part segmentation. However, the prevailing strategy of full fine-tuning in downstream tasks leads …
3D Parameter-Efficient Fine-Tuning for Classification3D Point Cloud ClassificationFew-Shot 3D Point Cloud ClassificationVisual Prompt Tuning