**Fast R-CNN** is an object detection model that improves in its predecessor [R-CNN](https://paperswithcode.com/method/r-cnn) in a number of ways. Instead of extracting CNN features independently for each region of interest, Fast R-CNN aggregates them into a single forward pass over the image; i.e. regions of interest from the same image share computation and memory in the forward and backward passes.

**Fast Voxel Query** is a module used in the [Voxel Transformer](https://paperswithcode.com/method/votr) 3D object detection model implementation of self-attention, specifically Local and Dilated Attention. For each querying index $v\_{i}$, an attending voxel index $v\_{j}$ is determined by Local and Dilated Attention. Then we can lookup the non-empty index $j$ in the hash table with hashed $v\_{j}$ as the key. Finally, the non-empty index $j$ is used to gather the attending feature $f\_{j}$ from $\mathcal{F}$ for [multi-head attention](https://paperswithcode.com/method/multi-head-attention).

Fast Voxel Query

Voxel Transformer for 3D Object Detection

Fast R-CNN

**SpreadsheetCoder** is a neural network architecture for spreadsheet formula prediction. It is a [BERT](https://paperswithcode.com/method/bert)-based model architecture to represent the tabular context in both row-based and column-based formats. A [BERT](https://paperswithcode.com/method/bert) encoder computes an embedding vector for each input token, incorporating the contextual information from nearby rows and columns. The BERT encoder is initialized from the weights pre-trained on English text corpora, which is beneficial for encoding table headers. To handle cell references, a two-stage decoding process is used inspired by sketch learning for program synthesis. The decoder first generates a formula sketch, which does not include concrete cell references, and then predicts the corresponding cell ranges to generate the complete formula

Source	Fast R-CNN
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com