Papers › Modeling the Background for Incremental Learning in Semantic Segmentation

Modeling the Background for Incremental Learning in Semantic Segmentation

3 Feb 2020CVPR 2020 6arXiv:2002.00718archive 2025-07-28

Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo

Despite their effectiveness in a wide range of tasks, deep architectures suffer from some important limitations. In particular, they are vulnerable to catastrophic forgetting, i.e. they perform poorly when they are required to update their model as new classes are available but the original training set is not retained. This paper addresses this problem in the context of semantic segmentation. Current strategies fail on this task because they do not consider a peculiar aspect of semantic segmentation: since each training step provides annotation only for a subset of all possible classes, pixels of the background class (i.e. pixels that do not belong to any other classes) exhibit a semantic distribution shift. In this work we revisit classical incremental learning methods, proposing a new distillation-based framework which explicitly accounts for this shift. Furthermore, we introduce a novel strategy to initialize classifier's parameters, thus preventing biased predictions toward the background class. We demonstrate the effectiveness of our approach with an extensive evaluation on the Pascal-VOC 2012 and ADE20K datasets, significantly outperforming state of the art incremental learning methods.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2002.00718")

Code

Syntology Ran 1 of 7 code samples harvested from 1 repository linked to this paper; 6 have no recorded run. Of those that ran: 1 ran with no contract checked.

By repository: named in the paper: 7 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

fcdl94/MiB mentioned in paperpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

7 samples harvested; 1 ran; 0 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran
6unverified

Licence: 0 of the 7 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from fcdl94/MiB. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

flip fcdl94/MiB/segmentation_module.py named in the paper ran fingerprinted MIT (permissive) · 7f8043e7dd6c2735 · report
filter_images fcdl94/MiB/dataset/utils.py named in the paper unverified MIT (permissive) · 21c634fc9f57dd4c · report
get_loss fcdl94/MiB/utils/loss.py named in the paper unverified MIT (permissive) · 1d5ab3d2728d692e · report
get_per_task_classes fcdl94/MiB/tasks.py named in the paper unverified MIT (permissive) · 1803646c9607e58b · report
get_task_labels fcdl94/MiB/tasks.py named in the paper unverified MIT (permissive) · ad850a613acafcc0 · report
group_images fcdl94/MiB/dataset/utils.py named in the paper unverified MIT (permissive) · afc11e8a9f0184d3 · report
modify_command_options fcdl94/MiB/argparser.py named in the paper unverified MIT (permissive) · dcde5d7316c65773 · report

Tasks

Continual LearningDisjoint 10-1Disjoint 15-1Disjoint 15-5Domain 1-1Domain 11-1Domain 11-5Incremental LearningOverlapped 10-1SegmentationSemantic Segmentation

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Disjoint 10-1 PASCAL VOC 2012 MiB mIoU 6.9 #6 of 8 Archive leaderboard report
Disjoint 15-1 PASCAL VOC 2012 MiB mIoU 39.9 #7 of 9 Archive leaderboard report
Disjoint 15-5 PASCAL VOC 2012 MiB Mean IoU 65.9 #6 of 9 Archive leaderboard report
Overlapped 10-1 PASCAL VOC 2012 MiB mIoU 20.1 #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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections