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CascadePSP

2 papers tagged archive 2025-07-28

Introduced by Ho Kei Cheng et al. in CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

CascadePSP is a general segmentation refinement model that refines any given segmentation from low to high resolution. The model takes as input an initial mask that can be an output of any algorithm to provide a rough object location. Then the CascadePSP will output a refined mask. The model is designed in a cascade fashion that generates refined segmentation in a coarse-to-fine manner. Coarse outputs from the early levels predict object structure which will be used as input to the latter levels to refine boundary details.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Segmentation2
Semantic Segmentation2
4k1
Land Cover Classification1
Position1
Scene Parsing1
Video Semantic Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with CascadePSP: 2020 to 2023, peak 1 1 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Semantic Segmentation Models

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