Feature Extractors

Feature Fusion Module v2

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

Feature Fusion Module v2 is a feature fusion module from the M2Det object detection model, and is crucial for constructing the final multi-level feature pyramid. They use 1x1 convolution layers to compress the channels of the input features and use a concatenation operation to aggregate these feature map. FFMv2 takes the base feature and the largest output feature map of the previous Thinned U-Shape Module (TUM) – these two are of the same scale – as input, and produces the fused feature for the next TUM.

Source: M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Object Classification 1 50.00%
Object Detection 1 50.00%

Components


Component Type
1x1 Convolution
Convolutions

Categories