.png){kind=icon}
Return
Multi-scale prototype convolutional network for few-shot semantic segmentation
.png){kind=icon}
OA
AI
DOI：10.1371/journal.pone.0319905.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Few-shot semantic segmentation aims to accurately segment objects from a limited amount of annotated data, a task complicated by intra-class variations and prototype representation challenges. To address these issues, we propose the Multi-Scale Prototype Convolutional Network (MPCN). Our approach introduces a Prior Mask Generation (PMG) module, which employs dynamic kernels of varying sizes to capture multi-scale object features. This enhances the interaction between support and query features, thereby improving segmentation accuracy. Additionally, we present a Multi-Scale Prototype Extraction (MPE) module to overcome the limitations of MAP (Mean Average Precision). By augmenting support set features, assessing spatial importance, and utilizing multi-scale downsampling, we obtain a more accurate prototype set. Extensive experiments conducted on the PASCAL-5i and COCO-20i datasets demonstrate that our method achieves superior performance in both 1-shot and 5-shot settings.
Journal
IF:
2.6
Papers:
2.6W
Citations:
81.6W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more