**TabTransformer** is a deep tabular data modeling architecture for supervised and semi-supervised learning. The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. 

As an overview, the architecture comprises a column embedding layer, a stack of $N$ [Transformer](/method/transformer) layers, and a multi-layer perceptron (MLP). The contextual embeddings (outputted by the Transformer layer) are concatenated along with continuous features which is inputted to an MLP. The loss function is then minimized  to learn all the parameters in an end-to-end learning.

ORB-SLAM2 is a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city.

Source: [Mur-Artal and Tardos](https://arxiv.org/pdf/1610.06475v2.pdf)

Image source: [Mur-Artal and Tardos](https://arxiv.org/pdf/1610.06475v2.pdf)

ORB-SLAM2

ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras

TabTransformer

TabTransformer: Tabular Data Modeling Using Contextual Embeddings

**DEXTR**, or **Deep Extreme Cut**, obtains an object segmentation from its four extreme points: the left-most, right-most, top, and bottom pixels. The annotated extreme points are given as a guiding signal to the input of the network. To this end, we create a [heatmap](https://paperswithcode.com/method/heatmap) with activations in the regions of extreme points. We center a 2D Gaussian around each of the points, in order to create a single heatmap. The heatmap is concatenated with the RGB channels of the input image, to form a 4-channel input for the CNN. In order to focus on the object of interest, the input is cropped by the bounding box, formed from the extreme point annotations. To include context on the resulting
crop, we relax the tight bounding box by several pixels. After the pre-processing step that comes exclusively from the extreme clicks, the input consists of an RGB crop including an object, plus its extreme points. 

[ResNet](https://paperswithcode.com/method/resnet)-101 is chosen as backbone of the architecture. We remove the fully connected layers as well as the [max pooling](https://paperswithcode.com/method/max-pooling) layers in the last two stages to preserve acceptable output resolution for dense prediction, and we introduce atrous convolutions in the last two stages to maintain the same receptive field. After the last ResNet-101 stage, we introduce a pyramid scene parsing module to aggregate global context to the final feature map. The output of the CNN is a probability map representing whether a pixel belongs to the object that we want to segment or not. The CNN is trained to minimize the standard cross entropy loss, which takes into account that different classes occur with different frequency in a dataset.

Source	TabTransformer: Tabular Data Modeling Using Contextual Embeddings
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com