Methods › Computer Vision › Instance Segmentation Models › SCNet

SCNet

6 papers tagged archive 2025-07-28

Introduced by Thang Vu et al. in SCNet: Training Inference Sample Consistency for Instance Segmentation

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

Sample Consistency Network (SCNet) is a method for instance segmentation which ensures the IoU distribution of the samples at training time are as close to that at inference time. To this end, only the outputs of the last box stage are used for mask predictions at both training and inference. The Figure shows the IoU distribution of the samples going to the mask branch at training time with/without sample consistency compared to that at inference time.

PaperSource

Papers archive 2025-07-28

6 shown of 6, 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

14 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
Cloth-Changing Person Re-Identification2
Person Re-Identification2
CPU1
Decoder1
Diversity1
Image Enhancement1
Instance Segmentation1
Music Source Separation1
Object Detection1
Prompt Learning1
Semantic Segmentation1
Vocal Bursts Type Prediction1
compressed sensing1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with SCNet: 2020 to 2024, peak 2 2 0 2020: 1 paper 2020 2021: 1 paper 2021 2022: 0 papers 2022 2023: 2 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (6 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

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections