Research Paper#Computer Vision, Image Segmentation, Zero-Shot Learning, Evolutionary Algorithms🔬 ResearchAnalyzed: Jan 3, 2026 08:47
Evolving Prompts for Zero-Shot Reasoning Segmentation
Published:Dec 31, 2025 08:10
•1 min read
•ArXiv
Analysis
This paper introduces EVOL-SAM3, a novel zero-shot framework for reasoning segmentation. It addresses the limitations of existing methods by using an evolutionary search process to refine prompts at inference time. This approach avoids the drawbacks of supervised fine-tuning and reinforcement learning, offering a promising alternative for complex image segmentation tasks.
Key Takeaways
- •Proposes EVOL-SAM3, a zero-shot framework for reasoning segmentation.
- •Uses an evolutionary search process to refine prompts at inference time.
- •Outperforms static baselines and even fully supervised methods on the ReasonSeg benchmark in a zero-shot setting.
- •Addresses limitations of SFT and RL approaches.
Reference
“EVOL-SAM3 not only substantially outperforms static baselines but also significantly surpasses fully supervised state-of-the-art methods on the challenging ReasonSeg benchmark in a zero-shot setting.”