Feature Extractors

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 Architecture for Object Detection

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Object Detection 1 100.00%

Components


Component Type
Convolution
Convolutions

Categories