Methods › Computer Vision › Feature Extractors › FFMv1

Feature Fusion Module v1

FFMv1

2 papers tagged archive 2025-07-28

Introduced by Qijie Zhao et al. in M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

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

Feature Fusion Module v1 is a feature fusion module from the M2Det object detection model, and feature fusion modules are crucial for constructing the final multi-level feature pyramid. They use 1x1 convolution layers to compress the channels of the input features and use concatenation operation to aggregate these feature map. FFMv1 takes two feature maps with different scales in backbone as input, it adopts one upsample operation to rescale the deep features to the same scale before the concatenation operation.

PaperSourceSee Code · qijiezhao/M2Det

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

8 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
3D Feature Matching1
Decoder1
Object1
Object Detection1
Text Detection1
Text Spotting1
document understanding1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with FFMv1: 2018 to 2021, peak 1 1 0 2018: 1 paper 2018 2019: 0 papers 2019 2020: 0 papers 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

Feature Extractors

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