**PointASNL** is a non-local neural network for point clouds processing It consists of two general modules: adaptive sampling (AS) module and local-Nonlocal (L-NL) module. The AS module first re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud. The AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. The L-NL module capture the neighbor and long-range dependencies of the sampled point, and enables the learning process to be insensitive to noise.

**Orientation Regularized Network** (ORN) is a multi-view image fusion technique for pose estimation. It uses IMU orientations as a structural prior to mutually fuse the image features of each pair of joints linked by IMUs. For example, it uses the features of the elbow to reinforce those of the wrist based on the IMU at the lower-arm.

Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach

PointASNL

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling

**MobileBERT** is a type of inverted-bottleneck [BERT](https://paperswithcode.com/method/bert) that compresses and accelerates the popular BERT model. MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to various downstream NLP tasks via simple fine-tuning. It is trained by layer-to-layer imitating the inverted bottleneck BERT.

Source	PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com