**CenterMask** is an anchor-free instance segmentation method that adds a novel [spatial attention-guided mask](https://paperswithcode.com/method/spatial-attention-guided-mask) (SAG-Mask) branch to anchor-free one stage object detector ([FCOS](https://paperswithcode.com/method/fcos)) in the same vein with [Mask R-CNN](https://paperswithcode.com/method/mask-r-cnn). Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation mask on each detected box with the spatial attention map that helps to focus on informative pixels and suppress noise.

A **Non-Local Block** is an image block module used in neural networks that wraps a [non-local operation](https://paperswithcode.com/method/non-local-operation). We can define a non-local block as:

$$ \mathbb{z}\_{i} = W\_{z}\mathbb{y\_{i}} + \mathbb{x}\_{i} $$

where $y\_{i}$ is the output from the non-local operation and $+ \mathbb{x}\_{i}$ is a [residual connection](https://paperswithcode.com/method/residual-connection).

Non-Local Block

Non-local Neural Networks

CenterMask

CenterMask : Real-Time Anchor-Free Instance Segmentation

The **Self-Attention Generative Adversarial Network**, or **SAGAN**, allows for attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other.

Source	CenterMask : Real-Time Anchor-Free Instance Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com