Exploring Open-Vocabulary Semantic Segmentation without Human Labels

Understand

Semantic segmentation is a crucial task in computer vision that involves segmenting images into semantically meaningful regions at the pixel level.

• However, existing approaches often rely on expensive human annotations as supervision for model training, limiting their scalability to large, unlabeled datasets.
• To address this challenge, we present ZeroSeg, a novel method that leverages the existing pretrained vision-language (VL) model (e.g.
• CLIP) to train open-vocabulary zero-shot semantic segmentation models.