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

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

20 Jun 2022arXiv:2206.09667archive 2025-07-28

Ehtesham Iqbal, Sirojbek Safarov, Seongdeok Bang

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

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Tasks

Few-Shot Semantic SegmentationMeta-LearningSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Semantic Segmentation COCO-20i (1-shot) MSANet (ResNet-101) FB-IoU 51.09 #10 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (1-shot) MSANet (ResNet-101) Mean IoU 50.45 #10 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (1-shot) MSANet (ResNet-50) FB-IoU 48.03 #26 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (1-shot) MSANet (ResNet-50) Mean IoU 46.44 #26 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) MSANet (ResNet-101) FB-IoU 56.8 #13 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) MSANet (ResNet-101) Mean IoU 56.3 #13 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) MSANet (ResNet-50) FB-IoU 53.67 #37 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) MSANet (ResNet-50) Mean IoU 50.47 #37 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) MSANet (ResNet-101) FB-IoU 80.38 #13 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) MSANet (ResNet-101) Mean IoU 69.13 #13 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) MSANet (ResNet-50) FB-IoU 80.44 #18 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) MSANet (ResNet-50) Mean IoU 68.52 #18 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) MSANet (VGG-16) FB-IoU 78.01 #44 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) MSANet (VGG-16) Mean IoU 65.76 #44 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) MSANet (ResNet-101) FB-IoU 84.3 #11 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) MSANet (ResNet-101) Mean IoU 73.99 #11 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) MSANet (ResNet-50) FB-IoU 83.23 #16 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) MSANet (ResNet-50) Mean IoU 72.6 #16 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) MSANet (VGG-16) FB-IoU 80.5 #38 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) MSANet (VGG-16) Mean IoU 70.4 #38 of 96 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionGlobal Average PoolingMax PoolingReLUResidual BlockResidual Connection

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