Papers › Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly...
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation
Weifeng Ge, Sheng Guo, Weilin Huang, Matthew R. Scott
Weakly-supervised instance segmentation aims to detect and segment object instances precisely, given imagelevel labels only. Unlike previous methods which are composed of multiple offline stages, we propose Sequential Label Propagation and Enhancement Networks (referred as Label-PEnet) that progressively transform image-level labels to pixel-wise labels in a coarse-to-fine manner. We design four cascaded modules including multi-label classification, object detection, instance refinement and instance segmentation, which are implemented sequentially by sharing the same backbone. The cascaded pipeline is trained alternatively with a curriculum learning strategy that generalizes labels from high-level images to low-level pixels gradually with increasing accuracy. In addition, we design a proposal calibration module to explore the ability of classification networks to find key pixels that identify object parts, which serves as a post validation strategy running in the inverse order. We evaluate the efficiency of our Label-PEnet in mining instance masks on standard benchmarks: PASCAL VOC 2007 and 2012. Experimental results show that Label-PEnet outperforms the state-of-the-art algorithms by a clear margin, and obtains comparable performance even with the fully-supervised approaches.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image-level Supervised Instance Segmentation | PASCAL VOC 2012 val | Label-PEnet | mAP@0.25 | 49.2 | #11 of 13 | Archive leaderboard | report |
| Image-level Supervised Instance Segmentation | PASCAL VOC 2012 val | Label-PEnet | mAP@0.5 | 30.2 | #11 of 13 | Archive leaderboard | report |
| Image-level Supervised Instance Segmentation | PASCAL VOC 2012 val | Label-PEnet | mAP@0.75 | 12.9 | #11 of 13 | 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.
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