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Conditional Convolutions for Instance Segmentation

CondInst

5 papers tagged archive 2025-07-28

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

CondInst is a simple yet effective instance segmentation framework. It eliminates ROI cropping and feature alignment with the instance-aware mask heads. As a result, CondInst can solve instance segmentation with fully convolutional networks. CondInst is able to produce high-resolution instance masks without longer computational time. Extensive experiments show that CondInst can achieve even better performance and inference speed than Mask R-CNN. It can be a strong alternative to previous ROI-based instance segmentation methods. Code is at https://github.com/aim-uofa/AdelaiDet.

Source: Conditional Convolutions for Instance SegmentationSee Code · aim-uofa/AdelaiDet

Papers archive 2025-07-28

5 shown of 5, 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

6 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 Segmentation5
Semantic Segmentation5
Segmentation3
Video Instance Segmentation2
Contrastive Learning1
Panoptic Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with CondInst: 2020 to 2022, peak 3 3 0 2020: 1 paper 2020 2021: 1 paper 2021 2022: 3 papers 2022
Papers per year the archive tags with this method, by the paper's archive date (5 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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