15 citations · 51 across the 26 of their papers we have counts for
11 papers · 1 filter
Merging Vision Transformers from Different Tasks and Domains
Peng Ye, Chenyu Huang, Mingzhu Shen +4
This work targets to merge various Vision Transformers (ViTs) trained on different tasks (i.e., datasets with different object categories) or domains (i.e., datasets with the same…
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,…
Efficient Architecture Search via Bi-level Data Pruning
Chongjun Tu, Peng Ye, Weihao Lin +5
Improving the efficiency of Neural Architecture Search (NAS) is a challenging but significant task that has received much attention. Previous works mainly adopted the Differentiabl…
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions
Jingdong Zhang, Jiayuan Fan, Peng Ye +5
Multi-task dense prediction aims at handling multiple pixel-wise prediction tasks within a unified network simultaneously for visual scene understanding. However, cross-task featur…
Towards an end-to-end artificial intelligence driven global weather forecasting system
Kun Chen, Lei Bai, Fenghua Ling +9
The weather forecasting system is important for science and society, and significant achievements have been made in applying artificial intelligence (AI) to medium-range weather fo…
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
Yongqi Huang, Peng Ye, Xiaoshui Huang +4
Structural re-parameterization is a general training scheme for Convolutional Neural Networks (CNNs), which achieves performance improvement without increasing inference cost. As V…
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…
Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification
Jingyi Zhou, Jiamu Sheng, Jiayuan Fan +4
The effectiveness of spectral-spatial feature learning is crucial for the hyperspectral image (HSI) classification task. Diffusion models, as a new class of groundbreaking generati…
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…
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification
Lin Zhan, Jiayuan Fan, Peng Ye +1
Existing deep learning-based hyperspectral image (HSI) classification works still suffer from the limitation of the fixed-sized receptive field, leading to difficulties in distinct…
-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…