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Clipseg Rd64
CLIPSeg is an image segmentation model based on text and image prompts, supporting zero-shot and one-shot image segmentation tasks.
Downloads 62
Release Time : 11/4/2022
Model Overview
Proposed by Lüddecke et al., this model combines CLIP's vision-language understanding capability for image segmentation, particularly suitable for scenarios requiring rapid adaptation to new categories.
Model Features
Zero-shot Segmentation
Capable of performing segmentation tasks without category-specific training
Multimodal Prompting
Supports using both text and images as segmentation prompts
Lightweight Version
Compressed version with dimension reduced to 64, balancing performance and efficiency
Model Capabilities
Image Segmentation
Zero-shot Learning
Multimodal Understanding
Semantic Segmentation
Use Cases
Computer Vision
Interactive Image Editing
Quickly select specific objects in images for editing via text prompts
Achieves precise object-level image manipulation
Visual Question Answering Systems
Locate relevant regions in images based on textual questions
Enhances interpretability of visual QA systems
Medical Imaging
Lesion Area Annotation
Assist medical image analysis using natural language descriptions
Reduces need for professional annotation
PyTorch
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