**Kernel Inducing Points**, or **KIP**, is a meta-learning algorithm for learning datasets that can mitigate the challenges which occur for naturally occurring datasets without a significant sacrifice in performance. KIP uses kernel-ridge regression to learn $\epsilon$-approximate datasets. It can be regarded as an adaption of the inducing point method for Gaussian processes to the case of Kernel Ridge Regression.

**Bottom-up Path Augmentation** is a feature extraction technique that seeks to shorten the information path and enhance a feature pyramid with accurate localization signals existing in low-levels. This is based on the fact that high response to edges or instance parts is a strong indicator to accurately localize instances. 

Each building block takes a higher resolution feature map $N\_{i}$ and a coarser map $P\_{i+1}$ through lateral connection and generates the new feature map $N\_{i+1}$ Each feature map $N\_{i}$ first goes through a $3 \times 3$ convolutional layer with stride $2$ to reduce the spatial size. Then each element of feature map $P\_{i+1}$ and the down-sampled map are added through lateral connection. The fused feature map is then processed by another $3 \times 3$ convolutional layer to generate $N\_{i+1}$ for following sub-networks. This is an iterative process and terminates after approaching $P\_{5}$. In these building blocks, we consistently use channel 256 of feature maps. The feature grid for each proposal is then pooled from new feature maps, i.e., {$N\_{2}$, $N\_{3}$, $N\_{4}$, $N\_{5}$}.

Bottom-up Path Augmentation

Path Aggregation Network for Instance Segmentation

Dataset Meta-Learning from Kernel Ridge-Regression

Our main contribution is the Guiding Points Network, where we integrate all information from the conditions to generate guiding points. By applying transformation matrices to scene entities (human/objects) with attention weighting, we can forecast the spanning of the target object.

Source	Dataset Meta-Learning from Kernel Ridge-Regression
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com