FFMv1

2000년 도입 · 논문 2편에서 사용

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.

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

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