这里我列了两个比较经典的拓展,一个是Visual Prompt Tuning (VPT),它是将 Prompt Tuning 引入了 ViT,分为两种,VPT-Deep 是在每层 Transformer 编码层输入前添加可学习 token 序列,VPT-Shallow 是只在第一层输入前添加。另一个是Context Optimization(CoOp),它是将 Prompt Tuning 引入了 CLIP 这种视觉-语言模型...
Set the concat sampling probability. This depends on the number of files being passed in the train set and how much percentage of the fine tuning data would you like to use from each file. Note sum of concat sampling probabilities should be 1.0. For example, the following is an example f...
CLIPFit README.md README Pytorch implementation of Paper: Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification (EMNLP 2024 Main) All the experiments are able to run on an A100 GPU. Installation Preparation. Our code is built on CoOp, so please followCoOpto install the ...
Why Parameter Efficient? Pre-training, then fully fine-tuning is a long standing paradigm in deep learning. However, as pre-trained models are scaling up, e.g. GPT-3(175B params), fully fine-tuning them on various downstream tasks has a high risk of overfitting. Moreover, in practice, ...
Time-, Memory- and Parameter-Efficient Visual Adaptation Otniel-Bogdan Mercea1, 2* Alexey Gritsenko1 Cordelia Schmid1 1Google 2University of Tu¨bingen Anurag Arnab1† Abstract As foundation models become more popular, there is a growing need to efficiently finetune them for ...
Through extensive experiments, we built our model by performing parameter-efficient fine-tuning of a ViT model pre-trained on a large-scale biomedical dataset. Attention rollouts indicated that the contours and internal features of the compressed vertebral body were critical in predicting VC with ...
Parameter-efficient fine-tuning (PEFT) methods are increasingly used with pre-trained language models (PLMs) for continual learning (CL). These methods typ... V Araujo,MF Moens,T Tuytelaars 被引量: 0发表: 2024年 Convolutional Prompting meets Language Models for Continual Learning Continual Learn...
参数高效微调已经用在了BERT、GPT、ViT、CLIP、Stable Diffusion等预训练下游任务上了 分为三个类别: prefix tuning re-parameterization adapters 提出的LLaMA-Adapter V2将prefix-tuning和adapter结合起来了。 通过使用early fusion策略和偏置tuning,LLaMA-Adapter V2将视觉特征注入到LLM中,产生了很好的多模态instruction-...
Additionally, published Med-VQA datasets are small that may lead to overfitting when directly fine-tuning on them. In this article, we integrate two designs and propose an efficient transfer learning method for Med-VQA named VQA-Adapter. To alleviate training costs, we introduce a novel and ...
This repository is the official implementation of "Parameter-efficient Prompt Learning for 3D Point Cloud Understanding", created by Hongyu Sun, Yongcai Wang, Wang Chen, Haoran Deng and Deying Li. Our work presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-mo...