3 accepted papers
In this work, we address the challenging issue of few-shot segmentation. Existing methods mainly explore the target object through the semantic similarity between the query and support pixels. However, the semantic similarity often fails to deal well with the target objects with large variations in…
Few-shot segmentation has got a lot of concerns recently. Existing methods mainly locate and recognize the target object based on a cross-guided way that applies masked target object features of support(query) images to make a feature matching with query(support) images. However, there are some diff…
The deep autoencoder network which is based on constraining non-negative weights, can learn a low dimensional part-based representation. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intraclass sample. Then one hopes to learn a new low…