Browse State-of-the-Art › Uncertainty-Aware Panoptic Segmentation
Uncertainty-Aware Panoptic Segmentation
3 papers with code · 1 benchmark · 1 dataset archive 2025-07-28
The ternary difficulty levels assigned to each pixel during the annotation of MUSES enable a novel task: uncertainty-aware panoptic segmen- tation. In this task, a panoptic segmentation model is compensated for errors in difficult image regions if it predicts the difficulty level correctly. To incorporate this idea into evaluation, we introduce the uncertainty-aware panoptic quality (UPQ) metric, an extension of panoptic quality.
Description from the archive archive 2025-07-28.
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.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
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| MUSES: MUlti-SEnsor Semantic perception dataset (1 row) | Mask2Former (Swin-T) | MUSES: The Multi-Sensor Semantic Perception Dataset for Driving... | code | Syntology ran 12 of 13 samples · 1 unverified | Compare |
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
3 shown of 3 papers with code (4 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.
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23 Jan 2024 1 repository listed Syntology ran 12 of 13 samples · 1 unverified · 13 pointer-only (licence)Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions.
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10 Oct 2022 1 repository listedCurrent learning-based methods typically try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncertainties.
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29 Jun 2022 1 repository listedIn this work, we introduce the novel task of uncertainty-aware panoptic segmentation, which aims to predict per-pixel semantic and instance segmentations, together with per-pixel uncertainty estimates.
Syntology lines on 1 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.
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