4 citations · 12 across the 12 of their papers we have counts for
12 papers
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…
EMR-Merging: Tuning-Free High-Performance Model Merging
Chenyu Huang, Peng Ye, Tao Chen +3
The success of pretrain-finetune paradigm brings about the release of numerous model weights. In this case, merging models finetuned on different tasks to enable a single model wit…
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…
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Mingye Xu, Mutian Xu, Tong He +4
Masked Modeling (MM) has demonstrated widespread success in various vision challenges, by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes remains a…
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…
Reconstructing Hand-Held Objects from Monocular Video
Di Huang, Xiaopeng Ji, Xingyi He +5
This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained n…
CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm
Mingye Xu, Yali Wang, Yihao Liu +2
Point cloud completion aims to predict complete shape from its partial observation. Current approaches mainly consist of generation and refinement stages in a coarse-to-fine style.…