**RPDet**, or **RepPoints Detector**, is a anchor-free, two-stage object detection model based on deformable convolutions.  [RepPoints](https://paperswithcode.com/method/reppoints) serve as the basic object representation throughout the detection system. Starting from the center points, the first set of RepPoints is obtained via regressing offsets over the center points. The learning of these RepPoints is driven by two objectives: 1) the top-left and bottom-right points distance loss between the induced pseudo box and the ground-truth bounding box; 2) the object recognition loss of the subsequent stage.

A **Selective Kernel** unit is a bottleneck block consisting of a sequence of 1×1 [convolution](https://paperswithcode.com/method/convolution), SK convolution and 1×1 convolution. It was proposed as part of the [SKNet](https://paperswithcode.com/method/sknet) CNN architecture. In general, all the large kernel convolutions in the original bottleneck blocks in [ResNeXt](https://paperswithcode.com/method/resnext) are replaced by the proposed SK convolutions, enabling the network to choose appropriate receptive field sizes in an adaptive manner. 

In SK units, there are three important hyper-parameters which determine the final settings of SK convolutions: the number of paths $M$ that determines the number of choices of different kernels to be aggregated, the group number $G$ that controls the cardinality of each path, and the reduction ratio $r$ that controls the number of parameters in the fuse operator. One typical setting of SK convolutions is $\text{SK}\left[M, G, r\right]$ to be $\text{SK}\left[2, 32, 16\right]$.

Selective Kernel

Selective Kernel Networks

RPDet

RepPoints: Point Set Representation for Object Detection

**Linear discriminant analysis** (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

Extracted from [Wikipedia](https://en.wikipedia.org/wiki/Linear_discriminant_analysis)

**Source**:

Paper: [Linear Discriminant Analysis: A Detailed Tutorial](https://dx.doi.org/10.3233/AIC-170729)

Public version: [Linear Discriminant Analysis: A Detailed Tutorial](https://usir.salford.ac.uk/id/eprint/52074/)

Source	RepPoints: Point Set Representation for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com