Methods › Computer Vision › Instance Segmentation Models › BlendMask

BlendMask

2 papers tagged archive 2025-07-28

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

BlendMask is an instance segmentation framework built on top of the FCOS object detector. The bottom module uses either backbone or FPN features to predict a set of bases. A single convolution layer is added on top of the detection towers to produce attention masks along with each bounding box prediction. For each predicted instance, the blender crops the bases with its bounding box and linearly combine them according the learned attention maps. Note that the Bottom Module can take features either from ‘C’, or ‘P’ as the input.

Source: BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

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
Instance Segmentation2
Segmentation2
Semantic Segmentation2
GPU1
Multi-Task Learning1
Panoptic Segmentation1
Real-time Instance Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with BlendMask: 2020 to 2020, peak 2 2 0 2020: 2 papers 2020
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

Instance Segmentation Models

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