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.

**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.

CenterMask

CenterMask : Real-Time Anchor-Free Instance Segmentation

SAGAN

Self-Attention Generative Adversarial Networks

**Region of Interest Pooling**, or **RoIPool**, is an operation for extracting a small feature map (e.g., $7×7$) from each RoI in detection and segmentation based tasks. Features are extracted from each candidate box, and thereafter in models like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn), are then classified and bounding box regression performed.

The actual scaling to, e.g., $7×7$, occurs by dividing the region proposal into equally sized sections, finding the largest value in each section, and then copying these max values to the output buffer. In essence, **RoIPool** is [max pooling](https://paperswithcode.com/method/max-pooling) on a discrete grid based on a box.

Image Source: [Joyce Xu](https://towardsdatascience.com/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9)

Source	Self-Attention Generative Adversarial Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com