DiverGen: Enhancing Instance Segmentation with Wider, More Diverse Generative Data

In the world of computer vision, instance segmentation remains a data-hungry task. As models grow in complexity, the need for large, diverse datasets becomes increasingly critical to avoid overfitting and improve accuracy. However, manual annotation is costly and time-consuming, limiting the scale of available datasets. Recent efforts have explored using generative models to create synthetic data for augmentation, but these approaches often fall short of fully leveraging the potential of such models.

Introducing DiverGen

A team of researchers from various institutions has introduced DiverGen, a novel approach that aims to construct more effective generative datasets for instance segmentation. The key idea behind DiverGen is to expand the data distribution that the model can learn, thereby mitigating overfitting and enhancing performance.

Technical Overview

Distribution Discrepancy

The authors of DiverGen start by examining the role of generative data from the perspective of distribution discrepancy. They investigate how different types of data affect the distribution learned by the model. By expanding this distribution with diverse generative data, they aim to reduce overfitting and improve generalization.

Strategies for Enhancing Diversity

To achieve this, DiverGen employs several strategies to enhance the diversity of the generative data:

• Category Diversity: Ensuring that the synthetic data covers a wide range of object categories.
• Prompt Diversity: Using varied prompts to generate different instances of the same category.
• Generative Model Diversity: Employing multiple generative models to produce a more diverse set of synthetic images.

These strategies allow DiverGen to scale the dataset to millions of samples while maintaining or even improving model performance.

Implementation and Results

The researchers implemented DiverGen using state-of-the-art generative models and evaluated its effectiveness on the LVIS (Large Vocabulary Instance Segmentation) dataset, which is known for its challenging rare categories.

• Performance Metrics:
  • Box AP (Average Precision): +1.1 points improvement across all categories.
  • Mask AP: +1.1 points improvement across all categories.
  • For rare categories specifically:
    • Box AP: +1.9 points
    • Mask AP: +2.5 points

These results demonstrate that DiverGen significantly outperforms the strong baseline model X-Paste, highlighting its effectiveness in improving instance segmentation performance.

Architecture and Benchmarks

The architecture of DiverGen is designed to be modular and flexible. The generative models used include:

• StyleGAN: For generating high-quality synthetic images.
• Diffusion Models: To introduce variability and diversity in the generated data.

The researchers also conducted extensive experiments to fine-tune hyperparameters and ensure that the generated data aligns well with the real-world distribution.

Conclusion

DiverGen represents a significant step forward in leveraging generative models for instance segmentation. By expanding the data distribution and enhancing diversity, it effectively mitigates overfitting and improves model performance, especially on rare categories. The availability of the codebase further encourages reproducibility and future research in this area.