Papers › Self-Guided and Cross-Guided Learning for Few-Shot Segmentation
Self-Guided and Cross-Guided Learning for Few-Shot Segmentation
Bingfeng Zhang, Jimin Xiao, Terry Qin
Few-shot segmentation has been attracting a lot of attention due to its effectiveness to segment unseen object classes with a few annotated samples. Most existing approaches use masked Global Average Pooling (GAP) to encode an annotated support image to a feature vector to facilitate query image segmentation. However, this pipeline unavoidably loses some discriminative information due to the average operation. In this paper, we propose a simple but effective self-guided learning approach, where the lost critical information is mined. Specifically, through making an initial prediction for the annotated support image, the covered and uncovered foreground regions are encoded to the primary and auxiliary support vectors using masked GAP, respectively. By aggregating both primary and auxiliary support vectors, better segmentation performances are obtained on query images. Enlightened by our self-guided module for 1-shot segmentation, we propose a cross-guided module for multiple shot segmentation, where the final mask is fused using predictions from multiple annotated samples with high-quality support vectors contributing more and vice versa. This module improves the final prediction in the inference stage without re-training. Extensive experiments show that our approach achieves new state-of-the-art performances on both PASCAL-5i and COCO-20i datasets.
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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) | PFENet (SCL, ResNet-101) | Mean IoU | 37 | #71 of 85 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (5-shot) | PFENet (SCL, ResNet-101) | Mean IoU | 39.9 | #73 of 81 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | PFENet (SCL, ResNet-50) | FB-IoU | 71.9 | #74 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | PFENet (SCL, ResNet-50) | Mean IoU | 61.8 | #74 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | CANet (SCL, ResNet-50) | FB-IoU | 70.3 | #92 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | CANet (SCL, ResNet-50) | Mean IoU | 57.5 | #92 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | PFENet (SCL, ResNet-50) | FB-IoU | 72.8 | #80 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | PFENet (SCL, ResNet-50) | Mean IoU | 62.9 | #80 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | CANet (SCL, ResNet-50) | FB-IoU | 70.7 | #88 of 96 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | CANet (SCL, ResNet-50) | Mean IoU | 59.2 | #88 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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