The **Squeeze-and-Excitation Block** is an architectural unit designed to improve the representational power of a network by enabling it to perform dynamic channel-wise feature recalibration. The process is:

- The block has a convolutional block as an input.
- Each channel is "squeezed" into a single numeric value using [average pooling](https://paperswithcode.com/method/average-pooling).
- A dense layer followed by a [ReLU](https://paperswithcode.com/method/relu) adds non-linearity and output channel complexity is reduced by a ratio.
- Another dense layer followed by a sigmoid gives each channel a smooth gating function.
- Finally, we weight each feature map of the convolutional block based on the side network; the "excitation".

**Inception-C** is an image model block used in the [Inception-v4](https://paperswithcode.com/method/inception-v4) architecture.

Inception-C

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

Squeeze-and-Excitation Block

Squeeze-and-Excitation Networks

**PolarMask** is an anchor-box free and single-shot instance segmentation method. Specifically, PolarMask takes an image as input and predicts the distance from a sampled positive location (ie a candidate object's center) with respect to the object's contour at each angle, and then assembles the predicted points to produce the final mask. There are several benefits to the system: (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework (2) Two modules are designed (i.e. soft polar centerness and polar IoU loss) to sample high-quality center examples and optimize polar contour regression, making the performance of PolarMask does not depend on the bounding box prediction results and more efficient in training. (3) PolarMask is fully convolutional and can be embedded into most off-the-shelf detection methods.

Source	Squeeze-and-Excitation Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com