A **Groupwise Point Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) where we apply a [point convolution](https://paperswithcode.com/method/pointwise-convolution) groupwise (using different set of convolution filter groups).

Image Credit: [Chi-Feng Wang](https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728)

**Hi-LANDER** is a hierarchical [graph neural network](https://paperswithcode.com/methods/category/graph-models) (GNN) model that learns how to cluster a set of images into an unknown number of identities using an image annotated with labels belonging to a disjoint set of identities. The hierarchical GNN uses an approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level. Unlike fully unsupervised hierarchical clustering, the choice of grouping and complexity criteria stems naturally from supervision in the training set.

Hi-LANDER

Learning Hierarchical Graph Neural Networks for Image Clustering

Groupwise Point Convolution

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

**Adaptive Non-Maximum Suppression** is a non-maximum suppression algorithm that applies a dynamic suppression threshold to an instance according to the target density. The motivation is to find an NMS algorithm that works well for pedestrian detection in a crowd. Intuitively, a high NMS threshold keeps more crowded instances while a low NMS threshold wipes out more false positives. The adaptive-NMS thus applies a dynamic suppression strategy, where the threshold rises as instances gather and occlude each other and decays when instances appear separately. To this end, an auxiliary and learnable sub-network is designed to predict the adaptive NMS threshold for each instance.

Source	ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com