Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models (2303.04803v4)

Abstract: We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. This demonstrates that their internal representation space is highly correlated with open concepts in the real world. Text-image discriminative models like CLIP, on the other hand, are good at classifying images into open-vocabulary labels. We leverage the frozen internal representations of both these models to perform panoptic segmentation of any category in the wild. Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks. In particular, with COCO training only, our method achieves 23.4 PQ and 30.0 mIoU on the ADE20K dataset, with 8.3 PQ and 7.9 mIoU absolute improvement over the previous state of the art. We open-source our code and models at https://github.com/NVlabs/ODISE .

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

The paper “Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models” introduces ODISE, a framework that harnesses the capabilities of pre-trained text-to-image diffusion models for open-vocabulary panoptic segmentation. ODISE effectively integrates these diffusion models with discriminative text-image models to enhance the segmentation performance across various challenging tasks.

Key Contributions

ODISE leverages the internal representations of text-to-image diffusion models to perform segmentation tasks. These diffusion models, when trained on broad datasets, exhibit a strong correlation with real-world concepts, allowing them to generate high-quality image data. The semantic richness of their internal feature spaces makes them suitable for tasks requiring the classification of arbitrary concepts.

1. Integration of Diffusion and Discriminative Models: ODISE combines the generative power of diffusion models with the classification strength of discriminative models such as CLIP. This hybrid approach utilizes frozen representations to achieve superior segmentation performance.
2. Open-Vocabulary Performance: By using these integrated models, ODISE achieves significant improvements over previous methods. For instance, on the ADE20K dataset, it recorded an impressive 23.4 PQ and 30.0 mIoU with COCO training, marking an 8.3 PQ and 7.9 mIoU enhancement over previous state-of-the-art methods.
3. Implicit Captioning: To address the reliance on paired image-caption data, ODISE introduces an implicit captioner. This module produces implicit text embeddings, ensuring optimal feature extraction for various downstream tasks even in the absence of explicit captions.
4. Universal Applicability: The paper extends its evaluation across multiple datasets, including ADE20K and COCO, demonstrating the method's robustness and effectiveness in segmenting both seen and unseen categories.

Numerical Results and Comparisons

The paper presents compelling results showcasing the superiority of its approach. For instance, ODISE outperforms the concurrent baseline, MaskCLIP, by 8.3 PQ on ADE20K. In semantic segmentation, it provides a marked improvement in mIoU scores on datasets with diverse classes such as ADE20K and Pascal Context. This achievement underscores the utility of text-to-image diffusion features in enhancing segmentation quality.

Theoretical and Practical Implications

The research explores the use of text-to-image diffusion models beyond image generation, emphasizing their potential as feature extractors for recognition tasks. This insight opens up possibilities for similar applications across other domains, where understanding and classifying open-vocabulary content is critical.

Practically, this method could transform several applications such as autonomous driving, where real-time, accurate segmentation is required for safety and efficiency. The ability of the framework to generalize to unseen categories has profound implications for dynamically evolving environments where pre-defined labels are insufficient.

Future Directions

This paper paves the way for future exploration into the incorporation of other generative models with discriminative tasks, potentially extending to fields beyond vision such as natural language processing. Furthermore, addressing challenges such as the category ambiguity seen in current datasets could refine the model's performance further. New methods for improving the specificity and exclusivity of category definitions are prospective research avenues.

In conclusion, ODISE represents a significant step forward in leveraging diffusion models for segmentation tasks, marking a notable advancement in open-vocabulary recognition and offering valuable insights for future developments in the field.