arXiv:2605.26689v1 Announce Type: cross Abstract: Modern referring image segmentation pipelines couple a vision-language model (VLM) for grounding with a promptable segmenter such as the Segment Anything Model (SAM) for mask generation. Prior training-free instances of this recipe consistently trail fine-tuned and reinforcement-learning (RL)-tuned specialists, and it has been unclear whether the gap comes from the VLM's grounding, SAM's capacity, or the prompt. We show that the gap is dominated by prompt ambiguity: a VLM-proposed bounding box (bbox) leaves SAM to guess which pixels inside the
This research provides a critical step towards improving the performance of general-purpose visual reasoning systems, addressing a known bottleneck in combining vision-language models with segmentation models.
Improved prompting techniques for foundation models will lead to more robust and accurate AI applications, enhancing the reliability and utility of AI systems in various real-world scenarios.
The understanding of prompt ambiguity as a dominant factor in the performance gap for referring image segmentation pipelines has shifted, leading to more focused research on prompt optimization.
- · AI developers
- · Robotics
- · Computer vision researchers
- · Generative AI platforms
- · Teams relying solely on bounding box prompts
- · Inefficient VLM-SAM integration methods
More accurate and efficient image segmentation will be achievable with existing models and data.
This could accelerate the deployment of autonomous systems and advanced visual search capabilities.
It might indirectly reduce the energy footprint of vision-language tasks by needing fewer inference cycles for task completion.
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Read at arXiv cs.CL