4 citations · 12 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Stimulative Training++: Go Beyond The Performance Limits of Residual Networks
Peng Ye, Tong He, Shengji Tang +4
Residual networks have shown great success and become indispensable in recent deep neural network models. In this work, we aim to re-investigate the training process of residual ne…
-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Search
Peng Ye, Tong He, Baopu Li +3
Neural Architecture Search has attracted increasing attention in recent years. Among them, differential NAS approaches such as DARTS, have gained popularity for the search efficien…
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…