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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
Grid-LOGAT: Grid Based Local and Global Area Transcription for Video Question Answering—0
GSON: A Group-based Social Navigation Framework with Large Multimodal Model—0
ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation—0
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?—0
KUDA: Keypoints to Unify Dynamics Learning and Visual Prompting for Open-Vocabulary Robotic Manipulation—0
LaViP:Language-Grounded Visual Prompts—0
Learning Expressive Prompting With Residuals for Vision Transformers—0
Learning Visual Prompts for Guiding the Attention of Vision Transformers—0
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention—0
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation—0
MLLM-Search: A Zero-Shot Approach to Finding People using Multimodal Large Language Models—0
MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting—0
MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks—0
NVSMask3D: Hard Visual Prompting with Camera Pose Interpolation for 3D Open Vocabulary Instance Segmentation—0
On the low-shot transferability of [V]-Mamba—0
Open-Set Video-based Facial Expression Recognition with Human Expression-sensitive Prompting—0
Personalization Toolkit: Training Free Personalization of Large Vision Language Models—0
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs—0
Prompt-Guided Attention Head Selection for Focus-Oriented Image Retrieval—0
Prompting the Unseen: Detecting Hidden Backdoors in Black-Box Models—0
Query Efficient Black-Box Visual Prompting with Subspace Learning—0
RadSAM: Segmenting 3D radiological images with a 2D promptable model—0
Rethinking Sparse Lexical Representations for Image Retrieval in the Age of Rising Multi-Modal Large Language Models—0
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge—0
RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics—0
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