Dec 16, 2023 · Abstract:As a prominent parameter-efficient fine-tuning technique in NLP, prompt tuning is being explored its potential in computer vision.
As a prominent parameter-efficient fine-tuning technique in. NLP, prompt tuning is being explored its potential in com- puter vision.
In this work, we propose the Spatially Aligned-and-Adapted Visual Prompt model (SA 2 2 {}^{2} start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT VP), which learns a ...
SA2VP: Spatially Aligned-and-Adapted Visual Prompt ... This repository contains the official PyTorch implementation for SA2VP. model_img. Environment settings. We ...
The Spatially Aligned-and-Adapted Visual Prompt model (SA^2VP), which learns a two-dimensional prompt token map with equal (or scaled) size to the image ...
In this work, we propose the Spatially Aligned-and-Adapted Visual Prompt model (SA^2VP), which learns a two-dimensional prompt token map with equal (or scaled) ...
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Awesome-Prompt-Adapter-Learning-for-VLMs A curated list of prompt/adapter learning methods for vision-language models (eg, CLIP).
Visual Prompt Tuning (VPT) is an effective tuning method for adapting pretrained Vision Transformers (ViTs) to downstream tasks.
In this work, we propose the Spatially Aligned-and-Adapted Visual Prompt model (SA^2VP), which learns a two-dimensional prompt token map with equal (or ...