Methods › Computer Vision › RoI Feature Extractors › GRoIE

Generic RoI Extractor

GRoIE

2 papers tagged archive 2025-07-28

Introduced by Leonardo Rossi et al. in A novel Region of Interest Extraction Layer for Instance Segmentation

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

GroIE is an RoI extractor which intends to overcome the limitation of existing extractors which select only one (the best) layer from the FPN. The intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer introduces non-local building blocks and attention mechanisms to boost the performance.

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

5 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
Instance Segmentation2
Object Detection2
Semantic Segmentation2
Segmentation1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with GRoIE: 2020 to 2021, peak 1 1 0 2020: 1 paper 2020 2021: 1 paper 2021
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

RoI Feature Extractors

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