Add to ORKGThe current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) as an efficient and effective alternative to full fine-tuning for large-scale Transformer models in vision. Taking inspiration from recent advances in efficiently tuning large language models, VPT introduces only a small amount (less than 1% of model parameters) of trainable parameters in the input space while keeping the model backbone frozen. Via extensive experiments on a wide variety of downstream recognition tasks, we show that VPT achieves significant performance gains compared to other parameter efficient tuning protocols. Most importantly, VPT even outperforms...
Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on ...
The size of vision models has grown exponentially over the last few years, especially after the emer...
The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual...
Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning),...
Recent advancements have illuminated the efficacy of some tensorization-decomposition Parameter-Effi...
Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of...
Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning...
Recent work has explored the potential to adapt a pre-trained vision transformer (ViT) by updating o...
Pre-Trained Vision-Language Models (VL-PTMs) have shown promising capabilities in grounding natural ...
We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset ...
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural ...
The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of buildin...
We investigate the efficacy of visual prompting to adapt large-scale models in vision. Following the...
Models should have the ability to adapt to unseen data during test-time to avoid performance drop ca...
Since the rise of powerful large-scale pre-trained Vision-Language (VL) models, such as CLIP and ALI...
Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on ...
The size of vision models has grown exponentially over the last few years, especially after the emer...
The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual...
Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning),...
Recent advancements have illuminated the efficacy of some tensorization-decomposition Parameter-Effi...
Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of...
Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning...
Recent work has explored the potential to adapt a pre-trained vision transformer (ViT) by updating o...
Pre-Trained Vision-Language Models (VL-PTMs) have shown promising capabilities in grounding natural ...
We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset ...
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural ...
The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of buildin...
We investigate the efficacy of visual prompting to adapt large-scale models in vision. Following the...
Models should have the ability to adapt to unseen data during test-time to avoid performance drop ca...
Since the rise of powerful large-scale pre-trained Vision-Language (VL) models, such as CLIP and ALI...
Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on ...
The size of vision models has grown exponentially over the last few years, especially after the emer...
The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual...