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Clipseg Rd64
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Clipseg Rd64

Developed by CIDAS
CLIPSeg is an image segmentation model based on text and image prompts, supporting zero-shot and one-shot image segmentation tasks.
Image Segmentation
Transformers
Open Source License:Apache-2.0#Zero-shot Image Segmentation#Text-prompted Segmentation#Vision-Language Joint Model
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
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