Papers › Hybrid Mamba for Few-Shot Segmentation

Hybrid Mamba for Few-Shot Segmentation

29 Sep 2024arXiv:2409.19613archive 2025-07-28

Qianxiong Xu, Xuanyi Liu, Lanyun Zhu, Guosheng Lin, Cheng Long, Ziyue Li, Rui Zhao

Many few-shot segmentation (FSS) methods use cross attention to fuse support foreground (FG) into query features, regardless of the quadratic complexity. A recent advance Mamba can also well capture intra-sequence dependencies, yet the complexity is only linear. Hence, we aim to devise a cross (attention-like) Mamba to capture inter-sequence dependencies for FSS. A simple idea is to scan on support features to selectively compress them into the hidden state, which is then used as the initial hidden state to sequentially scan query features. Nevertheless, it suffers from (1) support forgetting issue: query features will also gradually be compressed when scanning on them, so the support features in hidden state keep reducing, and many query pixels cannot fuse sufficient support features; (2) intra-class gap issue: query FG is essentially more similar to itself rather than to support FG, i.e., query may prefer not to fuse support features but their own ones from the hidden state, yet the success of FSS relies on the effective use of support information. To tackle them, we design a hybrid Mamba network (HMNet), including (1) a support recapped Mamba to periodically recap the support features when scanning query, so the hidden state can always contain rich support information; (2) a query intercepted Mamba to forbid the mutual interactions among query pixels, and encourage them to fuse more support features from the hidden state. Consequently, the support information is better utilized, leading to better performance. Extensive experiments have been conducted on two public benchmarks, showing the superiority of HMNet. The code is available at https://github.com/Sam1224/HMNet.

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Weighted_GAP sam1224/hmnet/model/HMNet.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · df77cb77dfe58d6d · report
conv1x1 sam1224/hmnet/model/backbone_res.py official repository ran · our draft was wrong no licence file found · pointer only · 2a80220dabcb742a · report
conv3x3 sam1224/hmnet/model/resnet.py official repository ran · our draft was wrong no licence file found · pointer only · fac5364e2f53c6db · report
conv3x3 sam1224/hmnet/model/backbone_res.py official repository ran · our draft was wrong no licence file found · pointer only · 600ff2c45e0de056 · report
get_gram_matrix sam1224/hmnet/model/HMNet.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · 7feaf1fceef927fe · report
get_vgg16_layer sam1224/hmnet/model/PSPNet.py official repository ran · our draft was wrong no licence file found · pointer only · ad7ba6c4e516607e · report
make_layers sam1224/hmnet/model/vgg.py official repository ran · our draft was wrong no licence file found · pointer only · e31250b969b51841 · report
resnet18 sam1224/hmnet/model/resnet.py official repository ran no licence file found · pointer only · 40a78e38251055c6 · report
resnet34 sam1224/hmnet/model/resnet.py official repository ran no licence file found · pointer only · 4f6dd0ac332dd50d · report
vgg11 sam1224/hmnet/model/vgg.py official repository ran licence not identified · pointer only · 2067e75510fbe85e · report
vgg11_bn sam1224/hmnet/model/vgg.py official repository ran licence not identified · pointer only · bf96fb236a6b1683 · report
resnet18 sam1224/hmnet/model/backbone_res.py official repository unverified licence not identified · pointer only · a519b31d6756cf9e · report
weighted_dice_loss sam1224/hmnet/model/loss.py official repository unverified licence not identified · pointer only · 4708c105ce1b6276 · report

Tasks

Few-Shot Semantic SegmentationMamba

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Semantic Segmentation COCO-20i (1-shot) HMNet (ResNet-50) FB-IoU 74.5 #7 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (1-shot) HMNet (ResNet-50) Mean IoU 52.1 #7 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (1-shot) HMNet (VGG-16) FB-IoU 72.6 #13 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (1-shot) HMNet (VGG-16) Mean IoU 49.1 #13 of 85 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) HMNet (ResNet-50) FB-IoU 77.6 #6 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) HMNet (ResNet-50) Mean IoU 58.9 #6 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) HMNet (VGG-16) FB-IoU 75.5 #18 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation COCO-20i (5-shot) HMNet (VGG-16) Mean IoU 54.5 #18 of 81 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) HMNet (ResNet-50) FB-IoU 81.6 #6 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) HMNet (ResNet-50) Mean IoU 70.4 #6 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) HMNet (VGG-16) FB-IoU 79.2 #29 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (1-Shot) HMNet (VGG-16) Mean IoU 67.3 #29 of 105 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) HMNet (ResNet-50) FB-IoU 84.4 #10 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) HMNet (ResNet-50) Mean IoU 74.1 #10 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) HMNet (VGG-16) FB-IoU 82.6 #29 of 96 Archive leaderboard report
Few-Shot Semantic Segmentation PASCAL-5i (5-Shot) HMNet (VGG-16) Mean IoU 71.1 #29 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

AttentionMambaSoftmax

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