**Adaptive Feature Pooling** pools features from all levels for each proposal in object detection and fuses them for the following prediction. For each proposal, we map them to different feature levels. Following the idea of [Mask R-CNN](https://paperswithcode.com/method/adaptive-feature-pooling), [RoIAlign](https://paperswithcode.com/method/roi-align) is used to pool feature grids from each level. Then a fusion operation (element-wise max or sum) is utilized to fuse feature grids from different levels.

The motivation for this technique is that in an [FPN](https://paperswithcode.com/method/fpn) we assign proposals to different feature levels based on the size of proposals, which could be suboptimal if images with small differences are assigned to different levels, or if the importance of features is not strongly correlated to their level which they belong.

We are introducing a universal handwritten image augmentation method that is language-agnostic. This groundbreaking technique can be applied to handwritten images in any language worldwide, marking it as the first of its kind. There are four methods for handwritten images which are ThickOCR, ThinOCR, Elongate OCR, Line Erase OCR.

Handwritten OCR

Handwritten image augmentation

Adaptive Feature Pooling

Path Aggregation Network for Instance Segmentation

**Attention-augmented Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) with a two-dimensional relative self-attention mechanism that can replace convolutions as a stand-alone computational primitive for image classification. It employs [scaled-dot product attention](https://paperswithcode.com/method/scaled) and [multi-head attention](https://paperswithcode.com/method/multi-head-attention) as with [Transformers](https://paperswithcode.com/method/transformer).

It works by concatenating convolutional and attentional feature map. To see this, consider an original convolution operator with kernel size $k$, $F\_{in}$ input filters and $F\_{out}$ output filters. The corresponding attention augmented convolution can be written as"

$$\text{AAConv}\left(X\right) = \text{Concat}\left[\text{Conv}(X), \text{MHA}(X)\right] $$

$X$ originates from an input tensor of shape $\left(H, W, F\_{in}\right)$. This is flattened to become $X \in \mathbb{R}^{HW \times F\_{in}}$ which is passed into a multi-head attention module, as well as a convolution (see above).

Similarly to the convolution, the attention augmented convolution 1) is equivariant to translation and 2) can readily operate on inputs of different spatial dimensions.

Source	Path Aggregation Network for Instance Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com