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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 21–30 of 127 papers

TitleStatusHype
KUDA: Keypoints to Unify Dynamics Learning and Visual Prompting for Open-Vocabulary Robotic Manipulation—0
Chameleon: Fast-slow Neuro-symbolic Lane Topology ExtractionCode2
Towards Universal Text-driven CT Image SegmentationCode0
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth AmbiguityCode0
The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting—0
From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework—0
Personalization Toolkit: Training Free Personalization of Large Vision Language Models—0
Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling—0
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model AdaptationCode1
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual PromptingCode0
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