Better image segmentation with few examples by using multiple relevant feature maps and with MSANet
Better image segmentation with few examples by using multiple relevant feature maps and with MSANet
MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation
arXiv paper abstract https://arxiv.org/abs/2206.09667v1
arXiv PDF paper https://arxiv.org/pdf/2206.09667v1.pdf
GitHub https://github.com/AIVResearch/MSANet
Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples.
Prototype learning, where the support feature yields a single or several prototypes by averaging global and local object information, has been widely used in FSS.
… To extract abundant features … propose a Multi-Similarity and Attention Network (MSANet) including two novel modules, a multi-similarity module and an attention module.
The multi-similarity module exploits multiple feature-maps of support images and query images to estimate accurate semantic relationships.
The attention module instructs the network to concentrate on class-relevant information.
… achieves the state-of-the-art performance for all 4-benchmark datasets with mean intersection over union (mIoU) of 69.13%, 73.99%, 51.09%, 56.80%, respectively …
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