TMI: Text-to-Image Meets Image-to-Image for Complementary Data Synthesis to Boost Long-Tailed Instance Segmentation
arXiv:2607.08201v1 Announce Type: cross Abstract: Large-vocabulary instance segmentation is constrained by long-tailed category distributions and fine-grained inter-class ambiguity. While data synthesis offers a promising alternative, current paradigms have complementary limitations: text-to-image (T2I) methods inherit noisy pseudo-labels and struggle on rare classes, whereas copy-paste methods compromise contextual realism. To address these issues, we propose a hybrid pipeline coupling T2I generation with context-aware image-to-image (I2I) editing. The T2I branch provides broad category and s
This research provides a timely advancement in data synthesis techniques for computer vision, specifically addressing limitations in long-tailed data distribution for instance segmentation.
Improving data synthesis for rare categories is crucial for the development of more robust and unbiased AI systems, expanding their applicability in real-world, complex visual environments.
The ability to generate higher quality, contextually realistic synthetic data for long-tailed categories will accelerate the training of advanced instance segmentation models without requiring extensive manual annotation.
- · AI researchers
- · Computer vision companies
- · Autonomous systems developers
- · Robotics companies
- · Companies reliant on purely manual data annotation
- · Generative AI models with poor contextual realism
Advanced instance segmentation models become more capable and deployable in diverse, real-world scenarios.
Reduced data collection and annotation costs indirectly accelerate the development of AI applications across various industries.
More sophisticated synthetic data generation methods could lead to stronger privacy-preserving AI training, reducing reliance on sensitive real-world datasets.
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Read at arXiv cs.AI