# Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models

**Authors:** Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, Shalini De Mello  
**arXiv:** [2303.04803](https://arxiv.org/abs/2303.04803)  
**Date:** 2023-03-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.04803v1/

## Key points

- Open-vocabulary recognition in computer vision is a significant problem.
- Panoptic segmentation, which combines object instance parsing and scene semantics, is challenging.
- Current methods for open-vocabulary recognition rely on pre-trained text-image discriminative models.
- These models lack spatial and relational understanding necessary for scene-level comprehension.
- Text-to-image generation using diffusion models has revolutionized image synthesis.
- Diffusion models compute cross attention between text and visual representation during image generation.
- ODISE (Open Vocabulary DIffusion based panoptic SEgmentation) combines pre-trained text-image diffusion and discriminative models.
- ODISE aims to overcome limitations of existing methods in terms of spatial and relational understanding.
- ODISE outperforms previous state-of-the-art approaches in open vocabulary panoptic segmentation and semantic segmentation tasks.
- ODISE achieves significant improvements in performance metrics such as PQ (Panoptic Quality) and mIoU (mean Intersection over Union).

## Summary

In the field of computer vision, the problem of open-vocabulary recognition has gained significant attention. Open-vocabulary recognition aims to replicate human-like understanding by recognizing limitless categories in a scene. However, very few approaches have been able to provide a unified framework for parsing object instances and scene semantics simultaneously, known as panoptic segmentation. Most current methods for open-vocabulary recognition rely on pre-trained text-image discriminative models trained with large-scale data. While these models excel at classifying individual objects and pixels, they often lack spatial and relational understanding necessary for scene-level structural comprehension. For example, CLIP, a popular text-image discriminative model, has been shown to struggle with accurately identifying spatial relations between objects. On the other hand, text-to-image generation using diffusion models trained on Internet-scale data has revolutionized image synthesis. These diffusion models can generate high quality images based on diverse open vocabulary language descriptions. Interestingly, these models compute cross attention between the provided text and their internal visual representation during the image generation process which suggests that their internal representation may be well differentiated and correlated with high/mid level semantic concepts described by language. Motivated by this observation, the authors propose ODISE (Open Vocabulary DIffusion based panoptic SEgmentation), a novel approach that combines pre trained text image diffusion and discriminative models to perform open vocabulary panoptic segmentation. By leveraging the frozen representations of both types of models ODISE aims to overcome the limitations of existing methods in terms of spatial and relational understanding. The results show that ODISE outperforms previous state of the art approaches in both open vocabulary panoptic segmentation and semantic segmentation tasks with only COCO training data achieving significant improvements in performance metrics such as PQ (Panoptic Quality) and mIoU (mean Intersection over Union). For instance on the ADE20K dataset ODISE achieves a PQ of 23.4 and mIoU of 30 surpassing previous state of the art by 8 3 PQ and 7 9 mIoU respectively.