Visual Prompting
32 papers with code • 0 benchmarks • 0 datasets
Visual Prompting is the task of streamlining computer vision processes by harnessing the power of prompts, inspired by the breakthroughs of text prompting in NLP. This innovative approach involves using a few visual prompts to swiftly convert an unlabeled dataset into a deployed model, significantly reducing development time for both individual projects and enterprise solutions.
Benchmarks
These leaderboards are used to track progress in Visual Prompting
Most implemented papers
Diversity-Aware Meta Visual Prompting
We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone.
Explicit Visual Prompting for Low-Level Structure Segmentations
nifangbaage/explicit-visual-prompt
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CVPR 2023
Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i. e., the features from frozen patch embeddings and the input's high-frequency components.
Exploring the Benefits of Visual Prompting in Differential Privacy
ezzzli/prompt-pate
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ICCV 2023
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model.
BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
changdaeoh/blackvip
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CVPR 2023
In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters.
UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
soon-yau/upgpt
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Text-to-image models (T2I) such as StableDiffusion have been used to generate high quality images of people.
Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting
hsb1357173526/dynamic_visual_prompting
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In addition, we also experiment DVP with the recently popular adapter approach to keep the most parameters of PLMs intact when adapting to VL tasks, helping PLMs achieve a quick shift between single- and multi-modal tasks.
Fine-Grained Visual Prompting
ylingfeng/FGVP
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NeurIPS 2023
Previous works have suggested that incorporating visual prompts, such as colorful boxes or circles, can improve the ability of models to recognize objects of interest.
Fast Segment Anything
In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance.
Visual Instruction Inversion: Image Editing via Visual Prompting
thaoshibe/visii
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Given pairs of example that represent the "before" and "after" images of an edit, our goal is to learn a text-based editing direction that can be used to perform the same edit on new images.
Uncovering the Hidden Cost of Model Compression
This empirical investigation underscores the need for a nuanced understanding beyond mere accuracy in sparse and quantized settings, thereby paving the way for further exploration in Visual Prompting techniques tailored for sparse and quantized models.
