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Visual Prompting

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.

Papers

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Showing 101–127 of 127 papers

TitleStatusHype
Unifying Image Processing as Visual Prompting Question Answering—0
Visual and textual prompts for enhancing emotion recognition in video—0
Visual Prompting for One-shot Controllable Video Editing without Inversion—0
Visual Prompting in LLMs for Enhancing Emotion Recognition—0
Visual Prompting in Multimodal Large Language Models: A Survey—0
Visual Prompting with Iterative Refinement for Design Critique Generation—0
VPA: Fully Test-Time Visual Prompt Adaptation—0
VP Lab: a PEFT-Enabled Visual Prompting Laboratory for Semantic Segmentation—0
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis—0
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning—0
Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model—0
Fast Segment AnythingCode0
Exploring the Benefits of Visual Prompting in Differential PrivacyCode0
Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual PromptingCode0
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood PerspectiveCode0
Targeted Visual Prompting for Medical Visual Question AnsweringCode0
Towards Universal Text-driven CT Image SegmentationCode0
UICrit: Enhancing Automated Design Evaluation with a UICritique DatasetCode0
Towards Online Multi-Modal Social Interaction UnderstandingCode0
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual PromptingCode0
Unleashing the Power of Visual Prompting At the Pixel LevelCode0
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth AmbiguityCode0
Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object SegmentationCode0
Uncovering the Hidden Cost of Model CompressionCode0
Benchmarking Human and Automated Prompting in the Segment Anything ModelCode0
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual PromptsCode0
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image UnderstandingCode0
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