Papers › Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation
Zhitong Xiong, Haopeng Li, Xiao Xiang Zhu
Training semantic segmentation models with few annotated samples has great potential in various real-world applications. For the few-shot segmentation task, the main challenge is how to accurately measure the semantic correspondence between the support and query samples with limited training data. To address this problem, we propose to aggregate the learnable covariance matrices with a deformable 4D Transformer to effectively predict the segmentation map. Specifically, in this work, we first devise a novel hard example mining mechanism to learn covariance kernels for the Gaussian process. The learned covariance kernel functions have great advantages over existing cosine similarity-based methods in correspondence measurement. Based on the learned covariance kernels, an efficient doubly deformable 4D Transformer module is designed to adaptively aggregate feature similarity maps into segmentation results. By combining these two designs, the proposed method can not only set new state-of-the-art performance on public benchmarks, but also converge extremely faster than existing methods. Experiments on three public datasets have demonstrated the effectiveness of our method.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | DACM (VAT, ResNet-50) | FB-IoU | 69.4 | #46 of 85 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | DACM (VAT, ResNet-50) | Mean IoU | 43 | #46 of 85 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | DACM (ResNet-50) | FB-IoU | 68.9 | #61 of 85 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | DACM (ResNet-50) | Mean IoU | 40.6 | #61 of 85 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (5-shot) | DACM (VAT, ResNet-50) | FB-IoU | 72.9 | #44 of 81 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (5-shot) | DACM (VAT, ResNet-50) | Mean IoU | 49.2 | #44 of 81 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (5-shot) | DACM (ResNet-50) | FB-IoU | 71.6 | #49 of 81 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (5-shot) | DACM (ResNet-50) | Mean IoU | 48.1 | #49 of 81 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | FSS-1000 (1-shot) | DACM (ResNet-101) | Mean IoU | 90.8 | #1 of 24 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | FSS-1000 (1-shot) | DACM (ResNet-50) | Mean IoU | 90.7 | #2 of 24 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | FSS-1000 (5-shot) | DACM (ResNet-101) | Mean IoU | 91.7 | #1 of 22 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | FSS-1000 (5-shot) | DACM (ResNet-50) | Mean IoU | 91.6 | #2 of 22 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (VAT, ResNet-101) | FB-IoU | 79.4 | #14 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (VAT, ResNet-101) | Mean IoU | 69.1 | #14 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (ResNet-101) | FB-IoU | 78.9 | #25 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (ResNet-101) | Mean IoU | 67.5 | #25 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (VAT, ResNet-50) | FB-IoU | 78.6 | #33 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (VAT, ResNet-50) | Mean IoU | 66.8 | #33 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (ResNet-50) | FB-IoU | 77.8 | #46 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (ResNet-50) | Mean IoU | 65.7 | #46 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (VGG-16) | FB-IoU | 75.5 | #73 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | DACM (VGG-16) | Mean IoU | 61.8 | #73 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (VAT, ResNet-101) | FB-IoU | 83.1 | #14 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (VAT, ResNet-101) | Mean IoU | 73.3 | #14 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (VAT, ResNet-50) | FB-IoU | 81.7 | #23 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (VAT, ResNet-50) | Mean IoU | 71.7 | #23 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (ResNet-101) | FB-IoU | 81.5 | #27 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (ResNet-101) | Mean IoU | 71.4 | #27 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (ResNet-50) | FB-IoU | 81.3 | #33 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (ResNet-50) | Mean IoU | 70.9 | #33 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (VGG-16) | FB-IoU | 77.8 | #72 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | DACM (VGG-16) | Mean IoU | 65.7 | #72 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
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