**SCARLET** is a type of convolutional neural architecture learnt by the [SCARLET-NAS](https://paperswithcode.com/method/scarlet-nas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The three variants are SCARLET-A, SCARLET-B and SCARLET-C. The basic building block is MBConvs from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2). Squeeze-and-excitation layers are also experimented with.

**LV-ViT** is a type of [vision transformer](https://paperswithcode.com/method/vision-transformer) that uses token labelling as a training objective. Different from the standard training
objective of ViTs that computes the classification loss on an additional trainable class token, token labelling takes advantage of all the image patch tokens to compute the training loss in a dense manner. Specifically, token labeling reformulates the image classification problem into multiple token-level recognition problems and assigns each patch token with an individual location-specific supervision generated by a machine annotator.

LV-ViT

All Tokens Matter: Token Labeling for Training Better Vision Transformers

SCARLET

SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search

**MotionNet** is a system for joint perception and motion prediction based on a bird's eye view (BEV) map, which encodes the object category and motion information from 3D point clouds in each grid cell. MotionNet takes a sequence of LiDAR sweeps as input and outputs the bird's eye view (BEV) map. The backbone of MotionNet is a spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses.

Source	SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com