Papers › Mitigating Background Shift in Class-Incremental Semantic Segmentation
Mitigating Background Shift in Class-Incremental Semantic Segmentation
Gilhan Park, WonJun Moon, SuBeen Lee, Tae-Young Kim, Jae-Pil Heo
Class-Incremental Semantic Segmentation(CISS) aims to learn new classes without forgetting the old ones, using only the labels of the new classes. To achieve this, two popular strategies are employed: 1) pseudo-labeling and knowledge distillation to preserve prior knowledge; and 2) background weight transfer, which leverages the broad coverage of background in learning new classes by transferring background weight to the new class classifier. However, the first strategy heavily relies on the old model in detecting old classes while undetected pixels are regarded as the background, thereby leading to the background shift towards the old classes(i.e., misclassification of old class as background). Additionally, in the case of the second approach, initializing the new class classifier with background knowledge triggers a similar background shift issue, but towards the new classes. To address these issues, we propose a background-class separation framework for CISS. To begin with, selective pseudo-labeling and adaptive feature distillation are to distill only trustworthy past knowledge. On the other hand, we encourage the separation between the background and new classes with a novel orthogonal objective along with label-guided output distillation. Our state-of-the-art results validate the effectiveness of these proposed methods.
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Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Disjoint 15-1 | PASCAL VOC 2012 | MBS | mIoU | 78.1 | #1 of 9 | Archive leaderboard | report |
| Disjoint 15-5 | PASCAL VOC 2012 | MBS | Mean IoU | 79.0 | #1 of 9 | Archive leaderboard | report |
| Disjoint 19-1 | PASCAL VOC 2012 | MBS | mIoU | 82.8 | #1 of 1 | Archive leaderboard | report |
| Overlapped 10-1 | PASCAL VOC 2012 | MBS | mIoU | 77.19 | #1 of 13 | Archive leaderboard | report |
| Overlapped 100-10 | ADE20K | MBS | Mean IoU (test) | 44.5 | #1 of 6 | Archive leaderboard | report |
| Overlapped 100-5 | ADE20K | MBS | mIoU | 42.8 | #1 of 8 | Archive leaderboard | report |
| Overlapped 100-50 | ADE20K | MBS | mIoU | 45.7 | #1 of 7 | Archive leaderboard | report |
| Overlapped 15-1 | PASCAL VOC 2012 | MBS | mIoU | 80.6 | #1 of 13 | Archive leaderboard | report |
| Overlapped 15-5 | PASCAL VOC 2012 | MBS | Mean IoU (val) | 82.6 | #1 of 13 | Archive leaderboard | report |
| Overlapped 5-3 | PASCAL VOC 2012 | MBS | Mean IoU (test) | 78.1 | #1 of 4 | Archive leaderboard | report |
| Overlapped 50-50 | ADE20K | MBS | mIoU | 45.4 | #1 of 7 | 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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