Overlapped 100-50

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PLOP: Learning without Forgetting for Continual Semantic Segmentation
  23 Nov 2020 2 repositories listed
  classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes.
• Mitigating Background Shift in Class-Incremental Semantic Segmentation
  16 Jul 2024 1 repository listed
  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.
• Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation
  13 Oct 2022 1 repository listed Syntology ran 1 of 8 samples · 7 unverified
  In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift.
• Representation Compensation Networks for Continual Semantic Segmentation
  10 Mar 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting.
• SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
  22 Jun 2021 1 repository listed Syntology ran 6 of 12 samples · 6 unverified · 12 pointer-only (licence)
  While the recent CISS algorithms utilize variants of the knowledge distillation (KD) technique to tackle the problem, they failed to fully address the critical challenges in CISS causing the catastrophic forgetting; the…

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.