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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
Test-time Correction with Human Feedback: An Online 3D Detection System via Visual Prompting—0
MLLM-Search: A Zero-Shot Approach to Finding People using Multimodal Large Language Models—0
Prompting the Unseen: Detecting Hidden Backdoors in Black-Box Models—0
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning—0
Benchmarking Human and Automated Prompting in the Segment Anything ModelCode0
Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms—0
Visual Prompting in LLMs for Enhancing Emotion Recognition—0
GSON: A Group-based Social Navigation Framework with Large Multimodal Model—0
Cycle-Consistency Uncertainty Estimation for Visual Prompting based One-Shot Defect Segmentation—0
End-to-end Open-vocabulary Video Visual Relationship Detection using Multi-modal Prompting—0
Visual Prompting in Multimodal Large Language Models: A Survey—0
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood PerspectiveCode0
Rethinking Sparse Lexical Representations for Image Retrieval in the Age of Rising Multi-Modal Large Language Models—0
Targeted Visual Prompting for Medical Visual Question AnsweringCode0
Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model—0
Chat2Layout: Interactive 3D Furniture Layout with a Multimodal LLM—0
Affordance-Guided Reinforcement Learning via Visual Prompting—0
UICrit: Enhancing Automated Design Evaluation with a UICritique DatasetCode0
DegustaBot: Zero-Shot Visual Preference Estimation for Personalized Multi-Object Rearrangement—0
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge—0
Robust Adaptation of Foundation Models with Black-Box Visual Prompting—0
Towards Open-World Grasping with Large Vision-Language Models—0
RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics—0
Exploring the Zero-Shot Capabilities of Vision-Language Models for Improving Gaze Following—0
Learning Visual Prompts for Guiding the Attention of Vision Transformers—0
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