The paper introduces Grounded SAM, a new approach that combines Grounding DINO and the Segment Anything Model to address open-set segmentation, a crucial aspect of open-world visual perception. The model can accurately segment objects based on textual prompts, even if they have never been seen before. The key takeaways for engineers/specialists from the paper are: 1. Grounded SAM combines the strengths of Grounding DINO for object detection and SAM for zero-shot segmentation, outperforming existing models. 2. The model's potential extends beyond segmentation, enabling integration with other models for tasks like image annotation, image editing, and human motion analysis. Read full paper: https://arxiv.org/abs/2401.14159 Tags: Computer Vision, Open-World Visual Perception, Segmentation Models