**GroIE** is an RoI extractor which intends to overcome the limitation of existing extractors which select only one (the best) layer from the [FPN](https://paperswithcode.com/method/fpn). The intuition is that all the layers of FPN retain useful
information. Therefore, the proposed layer introduces non-local building blocks and attention mechanisms to boost the performance.

**PointAugment** is a an auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier.

PointAugment

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

GRoIE

A novel Region of Interest Extraction Layer for Instance Segmentation

**PaLM** (**Pathways Language Model**) uses a standard Transformer model architecture (Vaswani et al., 2017) in a decoder-only setup (i.e., each timestep can only attend to itself and past timesteps), with several modifications. PaLM is trained as a 540 billion parameter, densely activated, autoregressive Transformer on 780 billion tokens. PaLM leverages Pathways (Barham et al., 2022), which enables highly efficient training of very large neural networks across thousands of accelerator chips.

Image credit: [PaLM: Scaling Language Modeling with Pathways](https://paperswithcode.com/paper/palm-scaling-language-modeling-with-pathways-1)

Source	A novel Region of Interest Extraction Layer for Instance Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com