Researchers from the University of Wisconsin-Madison have introduced SEEM, a new approach to interactive image segmentation that uses a universal interface and multi-modal prompts.
SEEM is the first model to support multiple prompts such as points, masks, text, boxes, and even a referred region of another seemingly heterogeneous image.
It is capable of recognizing and labeling different objects in an image based on their semantic meaning, and can even recognize and segment objects that were never seen during training.
Experiments show that SEEM has strong performance on different segmentation tasks, including closed-set and open-set segmentations.
It is a powerful, state-of-the-art segmentation model, bringing computer vision closer to the advancements seen in language models.
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