Papers › Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation

Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation

13 Oct 2022arXiv:2210.06780archive 2025-07-28

Yuanwei Liu, Nian Liu, Xiwen Yao, Junwei Han

Few-shot semantic segmentation aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more effective category information from the support to match with the corresponding objects in query. However, they all ignored the category information gap between query and support images. If the objects in them show large intra-class diversity, forcibly migrating the category information from the support to the query is ineffective. To solve this problem, we are the first to introduce an intermediate prototype for mining both deterministic category information from the support and adaptive category knowledge from the query. Specifically, we design an Intermediate Prototype Mining Transformer (IPMT) to learn the prototype in an iterative way. In each IPMT layer, we propagate the object information in both support and query features to the prototype and then use it to activate the query feature map. By conducting this process iteratively, both the intermediate prototype and the query feature can be progressively improved. At last, the final query feature is used to yield precise segmentation prediction. Extensive experiments on both PASCAL-5i and COCO-20i datasets clearly verify the effectiveness of our IPMT and show that it outperforms previous state-of-the-art methods by a large margin. Code is available at https://github.com/LIUYUANWEI98/IPMT

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Tasks

Few-Shot Semantic SegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Semantic Segmentation COCO-20i (1-shot) IPMT (ResNet-50) Mean IoU 43 #47 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (1-shot) IPMT (ResNet-101) Mean IoU 42.6 #48 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) IPMT (ResNet-101) Mean IoU 47.9 #53 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) IPMT (ResNet-50) Mean IoU 47.5 #55 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) IPMT (ResNet-50) FB-IoU 77.1 #35 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) IPMT (ResNet-50) Mean IoU 66.8 #35 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) IPMT (ResNet-101) FB-IoU 78.5 #41 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) IPMT (ResNet-101) Mean IoU 66.1 #41 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) IPMT (ResNet-101) FB-IoU 80.3 #51 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) IPMT (ResNet-101) Mean IoU 69.2 #51 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) IPMT (ResNet-50) FB-IoU 81.4 #58 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) IPMT (ResNet-50) Mean IoU 68.2 #58 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

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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