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Clipseg Rd64 Refined
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Clipseg Rd64 Refined

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#Dimensionality reduction optimization
Downloads 10.0M
Release Time : 11/1/2022

Model Overview

Proposed by Lüddecke et al., this model adopts a more complex convolutional architecture specifically designed for zero-shot and one-shot image segmentation tasks.

Model Features

Zero-shot image segmentation
Performs image segmentation tasks directly without the need for training.
One-shot learning
Adapts to new tasks with only a few samples.
Optimized convolutional architecture
Reduces dimensionality to 64 and employs a more complex convolutional structure to enhance performance.

Model Capabilities

Image segmentation
Zero-shot learning
One-shot learning

Use Cases

Computer vision
Text-prompted image segmentation
Directly segments specific objects in images based on text descriptions.
Accurately segments the target objects described in the text.
Image-prompted image segmentation
Directly segments target objects based on example images.
Accurately segments target objects similar to the example images.
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