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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 76–100 of 127 papers

TitleStatusHype
BLINK: Multimodal Large Language Models Can See but Not Perceive—0
Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale ApproachCode1
Exploring the Transferability of Visual Prompting for Multimodal Large Language ModelsCode1
Finding Visual Task VectorsCode1
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation—0
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
On the low-shot transferability of [V]-Mamba—0
MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting—0
Tumor segmentation on whole slide images: training or prompting?—0
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal ModelsCode1
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs—0
Tune-An-Ellipse: CLIP Has Potential to Find What You WantCode1
Generative Multimodal Models are In-Context LearnersCode3
LaViP:Language-Grounded Visual Prompts—0
3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V—0
Tokenize Anything via PromptingCode2
EZ-CLIP: Efficient Zeroshot Video Action RecognitionCode1
ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNetCode1
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model PerspectiveCode1
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual PromptsCode0
T-Rex: Counting by Visual Prompting—0
Visual In-Context PromptingCode4
GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure SegmentationCode1
Towards Robust and Accurate Visual Prompting—0
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