The core ingredient of **CayleyNet** is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. The model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators.

Description adapted from: [CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters](https://arxiv.org/pdf/1705.07664.pdf)

**Dynamic R-CNN** is an object detection method that adjusts the label assignment criteria (IoU threshold) and the shape of regression loss function (parameters of Smooth L1 Loss) automatically based on the statistics of proposals during training. The motivation is that in previous two-stage object detectors, there is an inconsistency problem between the fixed network settings and the dynamic training procedure. For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and thus are harmful to training high quality detectors.

It consists of two components: Dynamic Label Assignment and Dynamic Smooth L1 Loss, which are designed for the classification and regression branches, respectively. 

For Dynamic Label Assignment, we want our model to be discriminative for high IoU proposals, so we gradually adjust the IoU threshold for positive/negative samples based on the proposals distribution in the training procedure. Specifically, we set the threshold as the IoU of the proposal at a certain percentage since it can reflect the quality of the overall distribution. 

For Dynamic Smooth L1 Loss, we want to change the shape of the regression loss function to adaptively fit the distribution change of error and ensure the contribution of high quality samples to training. This is achieved by adjusting the $\beta$ in Smooth L1 Loss based on the error distribution of the regression loss function, in which $\beta$ actually controls the magnitude of the gradient of small errors.

Dynamic R-CNN

Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

CayleyNet

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

**FFB6D** is a full flow bidirectional fusion network for 6D pose estimation of known objects from a single RGBD image. Unlike previous works that extract the RGB and point cloud features independently and fuse them in the final stage, FFB6D builds bidirectional fusion modules as communication bridges in the full flow of the two networks. In this way, the two networks can obtain complementary information from the other and learn representations containing rich appearance and geometry information of the scene.

Source	CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com