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APoLLo: Unified Adapter and Prompt Learning for Vision Language Models

APoLLo: Unified Adapter and Prompt Learning for Vision Language Models

4 December 2023
Sanjoy Chowdhury
Sayan Nag
Dinesh Manocha
    VLM
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Papers citing "APoLLo: Unified Adapter and Prompt Learning for Vision Language Models"

10 / 10 papers shown
Title
Vision-Language Models for Edge Networks: A Comprehensive Survey
Vision-Language Models for Edge Networks: A Comprehensive Survey
Ahmed Sharshar
Latif U. Khan
Waseem Ullah
Mohsen Guizani
VLM
62
3
0
11 Feb 2025
CLIP with Generative Latent Replay: a Strong Baseline for Incremental
  Learning
CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning
Emanuele Frascaroli
Aniello Panariello
Pietro Buzzega
Lorenzo Bonicelli
Angelo Porrello
Simone Calderara
VLM
CLL
35
3
0
22 Jul 2024
Consolidator: Mergeable Adapter with Grouped Connections for Visual
  Adaptation
Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation
Tianxiang Hao
Hui Chen
Yuchen Guo
Guiguang Ding
42
16
0
30 Apr 2023
Visual-Language Prompt Tuning with Knowledge-guided Context Optimization
Visual-Language Prompt Tuning with Knowledge-guided Context Optimization
Hantao Yao
Rui Zhang
Changsheng Xu
VLM
VPVLM
122
200
0
23 Mar 2023
Prompt-aligned Gradient for Prompt Tuning
Prompt-aligned Gradient for Prompt Tuning
Beier Zhu
Yulei Niu
Yucheng Han
Yuehua Wu
Hanwang Zhang
VLM
175
269
0
30 May 2022
BLIP: Bootstrapping Language-Image Pre-training for Unified
  Vision-Language Understanding and Generation
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Junnan Li
Dongxu Li
Caiming Xiong
S. Hoi
MLLM
BDL
VLM
CLIP
388
4,110
0
28 Jan 2022
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language
  Modeling
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
Renrui Zhang
Rongyao Fang
Wei Zhang
Peng Gao
Kunchang Li
Jifeng Dai
Yu Qiao
Hongsheng Li
VLM
184
384
0
06 Nov 2021
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
236
804
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
280
3,835
0
18 Apr 2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy
  Text Supervision
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Chao Jia
Yinfei Yang
Ye Xia
Yi-Ting Chen
Zarana Parekh
Hieu H. Pham
Quoc V. Le
Yun-hsuan Sung
Zhen Li
Tom Duerig
VLM
CLIP
293
3,683
0
11 Feb 2021
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