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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

Showing 125 of 127 papers

TitleStatusHype
Segment AnythingCode5
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language ModelsCode4
GPT4Scene: Understand 3D Scenes from Videos with Vision-Language ModelsCode4
Visual In-Context PromptingCode4
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4VCode4
Generative Multimodal Models are In-Context LearnersCode3
Explicit Visual Prompting for Low-Level Structure SegmentationsCode2
Visual Prompting via Image InpaintingCode2
Memory-Space Visual Prompting for Efficient Vision-Language Fine-TuningCode2
Exploring Visual Prompts for Adapting Large-Scale ModelsCode2
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
Chameleon: Fast-slow Neuro-symbolic Lane Topology ExtractionCode2
Attention Prompting on Image for Large Vision-Language ModelsCode2
Explicit Visual Prompting for Universal Foreground SegmentationsCode2
Tokenize Anything via PromptingCode2
Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language ModelsCode2
BlackVIP: Black-Box Visual Prompting for Robust Transfer LearningCode1
GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure SegmentationCode1
Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time AdaptationCode1
Fine-Grained Visual PromptingCode1
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model AdaptationCode1
Diversity-Aware Meta Visual PromptingCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
EZ-CLIP: Efficient Zeroshot Video Action RecognitionCode1
Exploring the Transferability of Visual Prompting for Multimodal Large Language ModelsCode1
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