MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation

TL;DR

MSANet introduces multi-similarity and attention modules to enhance feature extraction and focus on relevant information, significantly improving few-shot segmentation performance across multiple datasets.

Contribution

The paper proposes MSANet, a novel network with multi-similarity and attention modules, to better represent features and improve segmentation accuracy in few-shot learning.

Findings

Achieves state-of-the-art results on PASCAL-5i and COCO-20i datasets.

Outperforms existing methods in 1-shot and 5-shot settings.

Demonstrates the effectiveness of multi-similarity and attention modules in FSS.

Abstract

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 singleor several prototypes by averaging global and local object information, has been widely used in FSS. However, utilizing only prototype vectors may be insufficient to represent the features for all training data. To extract abundant features and make more precise predictions, we 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. The network is tested on standard FSS datasets, PASCAL-5i 1-shot, PASCAL-5i 5-shot, COCO-20i 1-shot, and COCO-20i 5-shot. The MSANet with the backbone of ResNet-101 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. Code is available at https://github.com/AIVResearch/MSANet