4 citations · 12 across the 10 of their papers we have counts for
7 papers · 1 filter
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision
Minglei Li, Peng Ye, Yongqi Huang +5
Parameter-efficient fine-tuning (PEFT) has become increasingly important as foundation models continue to grow in both popularity and size. Adapter has been particularly well-recei…
Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers
Peng Ye, Yongqi Huang, Chongjun Tu +4
Fine-tuning pre-trained foundation models has gained significant popularity in various research fields. Existing methods for fine-tuning can be roughly divided into two categories,…
Boosting Residual Networks with Group Knowledge
Shengji Tang, Peng Ye, Baopu Li +5
Recent research understands the residual networks from a new perspective of the implicit ensemble model. From this view, previous methods such as stochastic depth and stimulative t…
PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer
Honghui Yang, Wenxiao Wang, Minghao Chen +5
Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations. However, the former requires time-consuming sampling whil…
Ponder: Point Cloud Pre-training via Neural Rendering
Di Huang, Sida Peng, Tong He +3
We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering. Motivated by the fact that informative point cloud featur…
EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder
Xiaoshui Huang, Zhou Huang, Sheng Li +5
The pretrain-finetune paradigm has achieved great success in NLP and 2D image fields because of the high-quality representation ability and transferability of their pretrained mode…