OV-DINO is a foundation model designed for open-vocabulary detection and segmentation tasks. It employs language-aware selective fusion to enable zero-shot object detection, meaning it can detect and segment object categories it has never seen during training based on textual descriptions. The model achieves state-of-the-art results on benchmarks like MS COCO and LVIS for zero-shot object detection.