Methods › Computer Vision › Feature Pyramid Blocks › Exact Fusion Model

Exact Fusion Model

1 paper tagged archive 2025-07-28

Introduced by Chien-Yao Wang et al. in CSPNet: A New Backbone that can Enhance Learning Capability of CNN

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

Exact Fusion Model (EFM) is a method for aggregating a feature pyramid. The EFM is based on YOLOv3, which assigns exactly one bounding-box prior to each ground truth object. Each ground truth bounding box corresponds to one anchor box that surpasses the threshold IoU. If the size of an anchor box is equivalent to the field-of-view of the grid cell, then for the grid cells of the s-th scale, the corresponding bounding box will be lower bounded by the $(s − 1)$th scale and upper bounded by the (s + 1)th scale. Therefore, the EFM assembles features from the three scales.

PaperSource

Papers archive 2025-07-28

1 shown of 1, 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

4 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
Attribute1
Image Classification1
Object Detection1
Real-Time Object Detection1

Usage over time archive 2025-07-28

Papers per year tagged with Exact Fusion Model: 2019 to 2019, peak 1 1 0 2019: 1 paper 2019
Papers per year the archive tags with this method, by the paper's archive date (1 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 Pyramid Blocks

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