"Few-Shot Segmentation Network Robust to Background Interference" by Enze Ji, Yunxiao Chen et al.
Few-shot segmentation has gained significant attention owning to the effectiveness in segmenting unseen classes with a few annotated images. However, there exist two challenges in previous works. 1) They focus on extracting foreground features of support images to guide the segmentation of unseen classes, which causes the loss of useful information and obtains a limited representation of the overall context. 2) They inevitably produce a bias towards base (seen) classes due to the meta-training on the base dataset. That is, the segmentation performance of models can not be guaranteed when predi...
Source: uow.edu.au