Multi Granularity Segmentation

Multi-granularity segmentation aims to segment images or 3D scenes at multiple levels of detail, from coarse to fine, adapting to varying needs and user instructions. Current research focuses on developing models that seamlessly switch between granularities, often integrating large multimodal models or leveraging techniques like hierarchical clustering and multi-scale feature representations. This capability is crucial for improving the accuracy and versatility of various applications, including image captioning, 3D reconstruction, and natural language processing tasks, by enabling more nuanced and context-aware analysis of visual and textual data. The development of robust and efficient multi-granularity segmentation methods is driving advancements across multiple fields.