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MatrixNet

3 papers tagged archive 2025-07-28

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

MatrixNet is a scale and aspect ratio aware building block for object detection that seek to handle objects of different sizes and aspect ratios. They have several matrix layers, each layer handles an object of specific size and aspect ratio. They can be seen as an alternative to FPNs. While FPNs are capable of handling objects of different sizes, they do not have a solution for objects of different aspect ratios. Objects such as a high tower, a giraffe, or a knife introduce a design difficulty for FPNs: does one map these objects to layers according to their width or height? Assigning the object to a layer according to its larger dimension would result in loss of information along the smaller dimension due to aggressive downsampling, and vice versa.

MatrixNets assign objects of different sizes and aspect ratios to layers such that object sizes within their assigned layers are close to uniform. This assignment allows a square output convolution kernel to equally gather information about objects of all aspect ratios and scales. MatrixNets can be applied to any backbone, similar to FPNs. We denote this by appending a "-X" to the backbone, i.e. ResNet50-X.

Source: MatrixNets: A New Scale and Aspect Ratio Aware...See Code · arashwan/matrixnet

Papers archive 2025-07-28

3 shown of 3, 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
Binarization1
CPU1
Learning-To-Rank1
Object1
Object Detection1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with MatrixNet: 2020 to 2025, peak 1 1 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (3 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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