CLIPSeg: Zero-shot Segmentation

CLIPSeg is a Zero-shot image segmentation model that can find the outlines of various objects through text descriptions without any additional training.

Existing models can only recognize specific trained categories, so processing new objects required data labeling and retraining, but CLIPSeg overcomes this limitation.

Key Features and How It Works:

• Using CLIP: Based on OpenAI's CLIP model, it maps images and text into the same high-dimensional embedding space. It identifies objects by measuring the similarity between images and text descriptions.
• Versatility: Not limited to specific categories, it can segment almost any object that can be entered as text.
• Applications: Applicable to various computer vision fields such as robot perception, image inpainting, and object detection.

Limitations and Improvements:

• The current model uses a 352x352 resolution, which may result in low precision.
• To compensate for this, you can fine-tune a model like SegFormer or use a tool like Segments.ai to refine the results.