Fig. 3. The pipeline of Hierarchical Feature Matching.

We average the tomogram features in the instance segmentation masks to obtain a query feature ${\mathbf{F}}_Q$. Then we downsample $\mathbf{F}$ into several coarse ones and match them with ${\mathbf{F}}_Q$ in a coarse-to-fine manner. After the last matching stage, we apply NMS and gather coordinates with top $K$ similarities as prompts to derive final semantic segmentation results.