Papers › Cascaded Sparse Feature Propagation Network for Interactive Segmentation

Cascaded Sparse Feature Propagation Network for Interactive Segmentation

10 Mar 2022arXiv:2203.05145archive 2025-07-28

Chuyu Zhang, Chuanyang Hu, Hui Ren, Yongfei Liu, Xuming He

We aim to tackle the problem of point-based interactive segmentation, in which the key challenge is to propagate the user-provided annotations to unlabeled regions efficiently. Existing methods tackle this challenge by utilizing computationally expensive fully connected graphs or transformer architectures that sacrifice important fine-grained information required for accurate segmentation. To overcome these limitations, we propose a cascade sparse feature propagation network that learns a click-augmented feature representation for propagating user-provided information to unlabeled regions. The sparse design of our network enables efficient information propagation on high-resolution features, resulting in more detailed object segmentation. We validate the effectiveness of our method through comprehensive experiments on various benchmarks, and the results demonstrate the superior performance of our approach. Code is available at \href{https://github.com/kleinzcy/CSFPN}{https://github.com/kleinzcy/CSFPN}.

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Foreground SegmentationInteractive SegmentationSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Interactive Segmentation Berkeley IA-FP-Net(HRNet, C+L) NoC@90 2.12 #6 of 14 Archive leaderboard report
Interactive Segmentation DAVIS IA-FP-Net NoC@85 4.03 #5 of 15 Archive leaderboard report
Interactive Segmentation DAVIS IA-FP-Net NoC@90 5.22 #5 of 15 Archive leaderboard report
Interactive Segmentation GrabCut IA-FP-Net NoC@90 1.68 #7 of 18 Archive leaderboard report
Interactive Segmentation SBD IA-FP-Net(HRNet,SBD) NoC@85 3.33 #4 of 14 Archive leaderboard report
Interactive Segmentation SBD IA-FP-Net(HRNet,SBD) NoC@90 5.25 #4 of 14 Archive leaderboard report

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