Seg-Agent: Training-Free Multimodal Reasoning for Language-Guided Segmentation
Seg-Agent is a framework for language-guided segmentation that eliminates the need for training and incorporates multimodal reasoning during testing. In contrast to current two-stage methods that depend on Multimodal Large Language Models (MLLMs) for interpreting commands and producing visual cues for foundational segmentation models like SAM, Seg-Agent overcomes the spatial grounding limitations of standard MLLMs without necessitating extensive training on large datasets. Its main innovation lies in a reasoning mechanism that functions in both textual and visual domains, allowing for improved segmentation accuracy based on natural language directives. This entirely training-free approach, outlined in arXiv:2605.12953, marks a notable progress in enhancing the accessibility and efficiency of language-guided segmentation.
Key facts
- Seg-Agent is a training-free framework for language-guided segmentation
- It integrates multimodal reasoning at test time
- Existing approaches use a two-stage framework with MLLMs and SAM
- Off-the-shelf MLLMs have limited spatial grounding capabilities
- Previous methods rely on extensive training on large-scale datasets
- Recent advances in reasoning operate only in the textual domain
- Seg-Agent incorporates direct visual feedback
- The paper is available on arXiv with ID 2605.12953
Entities
Institutions
- arXiv