Traditional semantic segmentation requires a large labeled image dataset and can only be predicted within predefined classes. Solving this problem of few-shot segmentation, which requires only a handful of annotations for the new target class, is important. However, with few-shot segmentation, the target class data distribution in the feature space is sparse and has low coverage because of the slight variations in the sample data. Setting the classification boundary that properly separates the target class from other classes is an impossible task. In particular, it is difficult to classify classes that are similar to the target class near the boundary. This study proposes the Interclass Prototype Relation Network (IPRNet), which improves the separation performance by reducing the similarity between other classes. We conducted extensive experiments with Pascal- 5 i and COCO- 20 i and showed that IPRNet provides the best segmentation performance compared with previous research.
CITATION STYLE
Okazawa, A. (2022). Interclass Prototype Relation for Few-Shot Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13689 LNCS, pp. 362–378). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19818-2_21
Mendeley helps you to discover research relevant for your work.