Papers › Where are the Masks: Instance Segmentation with Image-level Supervision
Where are the Masks: Instance Segmentation with Image-level Supervision
Issam H. Laradji, David Vazquez, Mark Schmidt
A major obstacle in instance segmentation is that existing methods often need many per-pixel labels in order to be effective. These labels require large human effort and for certain applications, such labels are not readily available. To address this limitation, we propose a novel framework that can effectively train with image-level labels, which are significantly cheaper to acquire. For instance, one can do an internet search for the term "car" and obtain many images where a car is present with minimal effort. Our framework consists of two stages: (1) train a classifier to generate pseudo masks for the objects of interest; (2) train a fully supervised Mask R-CNN on these pseudo masks. Our two main contribution are proposing a pipeline that is simple to implement and is amenable to different segmentation methods; and achieves new state-of-the-art results for this problem setup. Our results are based on evaluating our method on PASCAL VOC 2012, a standard dataset for weakly supervised methods, where we demonstrate major performance gains compared to existing methods with respect to mean average precision.
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Code
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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 | WISE | mAP@0.25 | 49.2 | #9 of 13 | Archive leaderboard | report |
| Image-level Supervised Instance Segmentation | PASCAL VOC 2012 val | WISE | mAP@0.5 | 41.7 | #9 of 13 | Archive leaderboard | report |
| Image-level Supervised Instance Segmentation | PASCAL VOC 2012 val | WISE | mAP@0.75 | 23.7 | #9 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.
Methods
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