The way AI visually understands images has evolved tremendously. Initially, AI could tell us "where" an object was using bounding boxes. Then, segmentation models arrived, precisely outlining an object's shape. More recently, open-vocabulary models emerged, allowing us to segment objects using less common labels like "blue ski boot" or "xylophone" without needing a predefined list of categories. P
We plan to create a very interesting demo by combining Grounding DINO and Segment Anything which aims to detect and segment anything with text inputs! And we will continue to improve it and create more interesting demos based on this foundation. And we have already released an overall technical report about our project on arXiv, please check Grounded SAM: Assembling Open-World Models for Diverse V
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![GitHub - IDEA-Research/GroundingDINO: [ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"](https://cdn-ak-scissors.b.st-hatena.com/image/square/3574292e2a3b5adb181786a76443d8d8a031e003/height=288;version=1;width=512/https%3A%2F%2Fsiteproxy.ruqli.workers.dev%3A443%2Fhttps%2Fopengraph.githubassets.com%2F05a34fe919b844f81b3c5b5ac093e2a6543c35694f175177ee12ba21dc09df12%2FIDEA-Research%2FGroundingDINO)
