Papers Visual Prompting
“Visual Prompting” 태그가 달린 논문 127편 · 필터 해제
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models (LMMs), such as GPT-4V. As illustrated in Fig. 1 (right), we employ off-the-shelf interact…
Interactive SegmentationReferring ExpressionReferring Expression ComprehensionSegmentation+2Unifying Image Processing as Visual Prompting Question Answering
Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for…
Image EnhancementImage RestorationIn-Context LearningQuestion Answering+2AutoVP: An Automated Visual Prompting Framework and Benchmark
Visual prompting (VP) is an emerging parameter-efficient fine-tuning approach to adapting pre-trained vision models to solve various downstream image-classification tasks. However, there has hitherto been little systemat…
image-classificationImage Classificationparameter-efficient fine-tuningVisual PromptingVPA: Fully Test-Time Visual Prompt Adaptation
Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-engineered prompts as trainable parameters. Ins…
Domain AdaptationPseudo LabelTest-time AdaptationVisual Prompting+2Uncovering the Hidden Cost of Model Compression
In an age dominated by resource-intensive foundation models, the ability to efficiently adapt to downstream tasks is crucial. Visual Prompting (VP), drawing inspiration from the prompting techniques employed in Large Lan…
modelModel CompressionQuantizationTransfer Learning+1ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation
While language-guided image manipulation has made remarkable progress, the challenge of how to instruct the manipulation process faithfully reflecting human intentions persists. An accurate and comprehensive description …
Image ManipulationPose TransferVideo InpaintingVisual PromptingVisual Instruction Inversion: Image Editing via Visual Prompting
Text-conditioned image editing has emerged as a powerful tool for editing images. However, in many situations, language can be ambiguous and ineffective in describing specific image edits. When faced with such challenges…
Visual PromptingFast Segment Anything
The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like image segmentation, image caption, and i…
Edge DetectionImage SegmentationInstance SegmentationObject Proposal Generation+4Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
Large language models (LLMs) have made significant advancements in natural language understanding. However, through that enormous semantic representation that the LLM has learnt, is it somehow possible for it to understa…
Few-Shot Learningimage-classificationImage ClassificationIn-Context Learning+5Fine-Grained Visual Prompting
Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception. However, these models have shown limited performance in instance-level tasks tha…
Visual PromptingAdapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting
Pre-trained language models (PLMs) have played an increasing role in multimedia research. In terms of vision-language (VL) tasks, they often serve as a language encoder and still require an additional fusion network for …
Transfer LearningVisual PromptingExplicit Visual Prompting for Universal Foreground Segmentations
Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works h…
Camouflaged Object SegmentationDefocus Blur DetectionForeground SegmentationImage Manipulation Detection+4FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation
Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model usi…
Domain AdaptationImage SegmentationMedical Image SegmentationSegmentation+3UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
Text-to-image models (T2I) such as StableDiffusion have been used to generate high quality images of people. However, due to the random nature of the generation process, the person has a different appearance e.g. pose, f…
DisentanglementImage GenerationPose TransferText-to-Image Generation+1Segment Anything
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with…
Event-based Object SegmentationImage SegmentationRobot Manipulation GeneralizationSegmentation+3Learning Expressive Prompting With Residuals for Vision Transformers
Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts …
Few-Shot Learningimage-classificationImage ClassificationPrompt Learning+2BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has gra…
Transfer LearningVisual PromptingExploring the Benefits of Visual Prompting in Differential Privacy
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. In this work, we explore the benefits of VP in co…
image-classificationImage ClassificationTransfer LearningVisual PromptingExplicit Visual Prompting for Low-Level Structure Segmentations
We consider the generic problem of detecting low-level structures in images, which includes segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow regions, and detecting concealed objects. W…
Camouflaged Object SegmentationDefocus Blur DetectionForeground SegmentationImage Manipulation Detection+3Diversity-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. A challenging issue in visual prompting…
DiversityVisual Prompting