**PAFPN** is a feature pyramid module used in Path Aggregation networks ([PANet](https://paperswithcode.com/method/panet)) that combines FPNs with [bottom-up path augmentation](https://paperswithcode.com/method/bottom-up-path-augmentation), which shortens the information path between lower layers and topmost feature.

**Locally-Grouped Self-Attention**, or **LSA**, is a local attention mechanism used in the [Twins-SVT](https://paperswithcode.com/method/twins-svt) architecture. Locally-grouped self-attention (LSA). Motivated by the group design in depthwise convolutions for efficient inference, we first equally divide the 2D feature maps into sub-windows, making self-attention communications only happen within each sub-window. This design also resonates with the multi-head design in self-attention, where the communications only occur within the channels of the same head. To be specific, the feature maps are divided into $m \times n$ sub-windows. Without loss of generality, we assume $H \% m=0$ and $W \% n=0$. Each group contains $\frac{H W}{m n}$ elements, and thus the computation cost of the self-attention in this window is $\mathcal{O}\left(\frac{H^{2} W^{2}}{m^{2} n^{2}} d\right)$, and the total cost is $\mathcal{O}\left(\frac{H^{2} W^{2}}{m n} d\right)$. If we let $k\_{1}=\frac{H}{n}$ and $k\_{2}=\frac{W}{n}$, the cost can be computed as $\mathcal{O}\left(k\_{1} k\_{2} H W d\right)$, which is significantly more efficient when $k\_{1} \ll H$ and $k\_{2} \ll W$ and grows linearly with $H W$ if $k\_{1}$ and $k\_{2}$ are fixed.

Although the locally-grouped self-attention mechanism is computation friendly, the image is divided into non-overlapping sub-windows. Thus, we need a mechanism to communicate between different sub-windows, as in Swin. Otherwise, the information would be limited to be processed locally, which makes the receptive field small and significantly degrades the performance as shown in our experiments. This resembles the fact that we cannot replace all standard convolutions by depth-wise convolutions in CNNs.

Locally-Grouped Self-Attention

Twins: Revisiting the Design of Spatial Attention in Vision Transformers

PAFPN

Path Aggregation Network for Instance Segmentation

**Dynamic R-CNN** is an object detection method that adjusts the label assignment criteria (IoU threshold) and the shape of regression loss function (parameters of Smooth L1 Loss) automatically based on the statistics of proposals during training. The motivation is that in previous two-stage object detectors, there is an inconsistency problem between the fixed network settings and the dynamic training procedure. For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and thus are harmful to training high quality detectors.

It consists of two components: Dynamic Label Assignment and Dynamic Smooth L1 Loss, which are designed for the classification and regression branches, respectively. 

For Dynamic Label Assignment, we want our model to be discriminative for high IoU proposals, so we gradually adjust the IoU threshold for positive/negative samples based on the proposals distribution in the training procedure. Specifically, we set the threshold as the IoU of the proposal at a certain percentage since it can reflect the quality of the overall distribution. 

For Dynamic Smooth L1 Loss, we want to change the shape of the regression loss function to adaptively fit the distribution change of error and ensure the contribution of high quality samples to training. This is achieved by adjusting the $\beta$ in Smooth L1 Loss based on the error distribution of the regression loss function, in which $\beta$ actually controls the magnitude of the gradient of small errors.

Source	Path Aggregation Network for Instance Segmentation
Year	2000
Data Source	Papers with Code archive (CC BY-SA)