Manyfew-shot segmentation (FSS) methods use cross attention to fuse support foreground (FG) into query features, regardless of the quadratic complexity.

Different finetune strategy: In Figure 1, we visualize the mIoU curve of different fine-tuning strategies. It can be seen that both layer-based and convolution-based fine-tuning methods bring over-fitting problems. This result shows that traditional fine-tuning methods are not suitable for few-shot segmentation tasks. Directly fine-tuning theparameters ofbackbone infew-shot learning affects the robustness ofFSS models. Therefore, we propose anovelfine-tuning strategy,namely SVF.

Previous works focus their efforts on exploring the support information while paying less attention to the mining of the critical query branch.

In this paper, we focus on utilizing pixel-wise relationships between support and query images to facilitate the few-shot segmentation task.