Title: Grounded Contrastive Learning for Open-world Semantic Segmentation. Abstract: Contrastive learning (CL) with large-scale image-text paired data has made great strides in open-world image recognition. The progress raises attraction to open-world semantic segmentation---aiming at learning to segment arbitrary visual concepts in images. Existing open-world segmentation methods adopt CL to learn diverse visual concepts and adapt its image-level understanding to the segmentation task. However, while CL-based existing methods have shown impressive results, conventional CL is limited in considering image-text level alignment without explicit optimization of region-text level alignment, thus leading to a sub-optimal solution for the segmentation task. In this paper, we propose a novel Grounded Contrastive Learning (GCL) framework to directly align a text and regions described by the text. Our method generates a segmentation mask associated with a given text, extracts grounded image embedding from the masked image region, and aligns it with text embedding via GCL. The framework encourages a model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. GCL achieves state-of-the-art zero-shot segmentation performance with large margins in all datasets. The code will be released publicly available.