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Consolidator: Mergeable Adapter with Grouped Connections for Visual
  Adaptation

Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation

30 April 2023
Tianxiang Hao
Hui Chen
Yuchen Guo
Guiguang Ding
ArXivPDFHTML

Papers citing "Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation"

5 / 5 papers shown
Title
AdaptFormer: Adapting Vision Transformers for Scalable Visual
  Recognition
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Shoufa Chen
Chongjian Ge
Zhan Tong
Jiangliu Wang
Yibing Song
Jue Wang
Ping Luo
138
631
0
26 May 2022
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally
  Across Scales and Tasks
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
Xiao Liu
Kaixuan Ji
Yicheng Fu
Weng Lam Tam
Zhengxiao Du
Zhilin Yang
Jie Tang
VLM
228
780
0
14 Oct 2021
The Power of Scale for Parameter-Efficient Prompt Tuning
The Power of Scale for Parameter-Efficient Prompt Tuning
Brian Lester
Rami Al-Rfou
Noah Constant
VPVLM
275
3,784
0
18 Apr 2021
RepVGG: Making VGG-style ConvNets Great Again
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding
X. Zhang
Ningning Ma
Jungong Han
Guiguang Ding
Jian-jun Sun
114
1,484
0
11 Jan 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
948
20,214
0
17 Apr 2017
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