**Inception-ResNet-v2-A** is an image model block for a 35 x 35 grid used in the [Inception-ResNet-v2](https://paperswithcode.com/method/inception-resnet-v2) architecture.

**PointRend** is a module for image segmentation tasks, such as instance and semantic segmentation, that attempts to treat segmentation as image rending problem to efficiently "render" high-quality label maps. It uses a subdivision strategy to adaptively select a non-uniform set of points at which to compute labels. PointRend can be incorporated into popular meta-architectures for both instance segmentation (e.g. [Mask R-CNN](https://paperswithcode.com/method/mask-r-cnn)) and semantic segmentation (e.g. [FCN](https://paperswithcode.com/method/fcn)). Its subdivision strategy efficiently computes high-resolution segmentation maps using an order of magnitude fewer floating-point operations than direct, dense computation.

PointRend is a general module that admits many possible implementations. Viewed abstractly, a PointRend module accepts one or more typical CNN feature maps $f\left(x\_{i}, y\_{i}\right)$ that are defined over regular grids, and outputs high-resolution predictions $p\left(x^{'}\_{i}, y^{'}\_{i}\right)$ over a finer grid. Instead of making excessive predictions over all points on the output grid, PointRend makes predictions only on carefully selected points. To make these predictions, it extracts a point-wise feature representation for the selected points by interpolating $f$, and uses a small point head subnetwork to predict output labels from the point-wise features.

PointRend

PointRend: Image Segmentation as Rendering

Inception-ResNet-v2-A

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

An **Accumulating Eligibility Trace** is a type of [eligibility trace](https://paperswithcode.com/method/eligibility-trace) where the trace increments in an accumulative way. For the memory vector $\textbf{e}\_{t} \in \mathbb{R}^{b} \geq \textbf{0}$:

$$\mathbf{e\_{0}} = \textbf{0}$$

$$\textbf{e}\_{t} = \nabla{\hat{v}}\left(S\_{t}, \mathbf{\theta}\_{t}\right) + \gamma\lambda\textbf{e}\_{t}$$

Source	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com