NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·1h ago
AI Research
arXiv CS.AI·25d ago·5 min read
TMI: Text-to-Image Meets Image-to-Image for Complementary Data Synthesis to Boost Long-Tailed Instance Segmentation
TL;DR
Long-tailed instance segmentation suffers from data imbalance, limiting model performance. A novel approach combines text-to-image and image-to-image synthesis to generate complementary data.
✦ Why It Matters
Implement TMI to generate synthetic data for training models on imbalanced datasets, improving segmentation outcomes.
Key Takeaways
- Explore hybrid data synthesis methods for better segmentation results.
- Utilize VRAIN for augmenting rare-class instances in your datasets.
- Focus on context-aware editing to enhance visual realism.
- Evaluate performance improvements using the LVIS benchmark.
How It Works
The proposed hybrid pipeline combines T2I generation, which creates diverse images based on textual prompts, with I2I editing that refines these images by ensuring contextual accuracy. The teacher-student scheme selectively retains categories specified in prompts, enhancing label reliability.
VRAIN, the I2I editor, strategically places high-confidence instances in semantically appropriate locations within existing scenes, resulting in coherent and realistic images.