Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.

SOHO (“See Out of tHe bOx”) that takes a whole image as input, and learns vision-language representation in an end-to-end manner. SOHO does not require bounding box annotations which enables inference 10 times faster than region-based approaches. Text embeddings are used to extract textual embedding features. A trainable CNN is used to extract visual representations. SOHO learns to extract comprehensive yet compact image features through a visual dictionary (VD) that facilitates cross-modal understanding. VD is designed to represent consistent visual abstractions of similar semantics. It is updated on-the-fly and utilized in the proposed pre-training task Masked Visual Modeling (MVM).

SOHO

Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Feature Selection

Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

**LiteSeg** is a lightweight architecture for semantic segmentation that uses a deeper version of Atrous [Spatial Pyramid Pooling](https://paperswithcode.com/method/spatial-pyramid-pooling) module ([ASPP](https://paperswithcode.com/method/aspp)) and applies short and long residual connections, and [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution), resulting in a faster, more efficient model.

Source	Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
Year	2000
Data Source	Papers with Code archive (CC BY-SA)