**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) layers.

**Guided Anchoring** is an anchoring scheme for object detection which leverages semantic features to guide the anchoring. The method is motivated by the observation that objects are not distributed evenly over the image. The scale of an object is also closely related to the imagery content, its location and geometry of the scene. Following this intuition, the method generates sparse anchors in two steps: first identifying sub-regions that may contain objects and then determining the shapes at different locations.

Guided Anchoring

Region Proposal by Guided Anchoring

Xception

Xception: Deep Learning With Depthwise Separable Convolutions

A **Spatial Attention Module** is a module for spatial attention in convolutional neural networks. It generates a spatial attention map by utilizing the inter-spatial relationship of features. Different from the [channel attention](https://paperswithcode.com/method/channel-attention-module), the spatial attention focuses on where is an informative part, which is complementary to the channel attention. To compute the spatial attention, we first apply average-pooling and max-pooling operations along the channel axis and concatenate them to generate an efficient feature descriptor. On the concatenated feature descriptor, we apply a [convolution](https://paperswithcode.com/method/convolution) layer to generate a spatial attention map $\textbf{M}\_{s}\left(F\right) \in \mathcal{R}^{H×W}$ which encodes where to emphasize or suppress. 

We aggregate channel information of a feature map by using two pooling operations, generating two 2D maps: $\mathbf{F}^{s}\_{avg} \in \mathbb{R}^{1\times{H}\times{W}}$ and $\mathbf{F}^{s}\_{max} \in \mathbb{R}^{1\times{H}\times{W}}$. Each denotes average-pooled features and max-pooled features across the channel. Those are then concatenated and convolved by a standard convolution layer, producing the 2D spatial attention map. In short, the spatial attention is computed as:

$$ \textbf{M}\_{s}\left(F\right) = \sigma\left(f^{7x7}\left(\left[\text{AvgPool}\left(F\right);\text{MaxPool}\left(F\right)\right]\right)\right) $$

$$ \textbf{M}\_{s}\left(F\right) = \sigma\left(f^{7x7}\left(\left[\mathbf{F}^{s}\_{avg};\mathbf{F}^{s}\_{max} \right]\right)\right) $$

where $\sigma$ denotes the sigmoid function and $f^{7×7}$ represents a convolution operation with the filter size of 7 × 7.

Source	Xception: Deep Learning With Depthwise Separable Convolutions
Year	2000
Data Source	Papers with Code archive (CC BY-SA)