Seg-Agent: Test-Time Multimodal Reasoning for Training-Free Language-Guided Segmentation
Language-guided segmentation transcends the scope limitations of traditional semantic segmentation, enabling models to segment arbitrary target regions based on natural language instructions. Existing approaches typically adopt a two-stage framework: employing Multimodal Large Language Models (MLLMs) to interpret instructions and generate visual prompts, followed by foundational segmentation models (e.g., SAM) to produce masks. However, due to the limited spatial grounding capabilities of off-the-shelf MLLMs, these methods often rely on extensive training on large-scale datasets to achieve satisfactory accuracy. While recent advances have introduced reasoning mechanisms to improve perform...
| Authors | Chao Hao, Jun Xu, Ji Du et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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