Remove Your Limits: This AI Approach Uses Diffusion Models to Enable Open-Voc

Object segmentation is an important problem in computer vision, with applications in autonomous driving, surveillance and robotics.

Recent advancements in deep learning have made it easier to solve, but challenging scenarios remain.

ODISE is the first work to explore large-scale text-to-image diffusion models for open-vocabulary segmentation tasks, leveraging both text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation.

It outperforms all existing baselines on many open-vocabulary recognition tasks.

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