**Feature Fusion Module v2** is a feature fusion module from the [M2Det](https://paperswithcode.com/method/m2det) object detection model, and is crucial for constructing the final multi-level feature pyramid. They use [1x1 convolution](https://paperswithcode.com/method/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](https://paperswithcode.com/method/tum) (TUM) – these two are of the same scale – as input, and produces the fused feature for the next TUM.

**Negative Face Recognition**, or **NFR**, is a face recognition approach that enhances the soft-biometric privacy on the template-level by representing face templates in a complementary (negative) domain. While ordinary templates characterize facial properties of an individual, negative templates describe facial properties that does not exist for this individual. This suppresses privacy-sensitive information from stored templates. Experiments are conducted on two publicly available datasets captured under controlled and uncontrolled scenarios on three privacy-sensitive attributes.

Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition

FFMv2

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

A **Selective Kernel Convolution** is a [convolution](https://paperswithcode.com/method/convolution) that enables neurons to adaptively adjust their RF sizes among multiple kernels with different kernel sizes. Specifically, the SK convolution has three operators – Split, Fuse and Select. Multiple branches with different kernel sizes are fused using
[softmax](https://paperswithcode.com/method/softmax) attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer

Source	M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com