Transforming Segmentation Through Language
Converting complex image segmentation into simple text generation
Text4Seg reimagines image segmentation by treating it as a text generation task, eliminating specialized decoders and simplifying integration with multimodal large language models.
- Introduces semantic descriptors to represent segmentation masks as text
- Employs Row-wise Run-Length Encoding (R-RLE) for efficient conversion between masks and text
- Demonstrates compatibility with existing MLLMs without specialized architectures
- Achieves competitive results while reducing engineering complexity
This engineering breakthrough matters because it streamlines the integration of advanced segmentation capabilities into language models, potentially enabling more versatile AI systems that can understand and interact with visual content through natural language.