What is: Bottom-up Path Augmentation?

Bottom-up Path Augmentation is a feature extraction technique that seeks to shorten the information path and enhance a feature pyramid with accurate localization signals existing in low-levels. This is based on the fact that high response to edges or instance parts is a strong indicator to accurately localize instances.

Each building block takes a higher resolution feature map $N\_{i}$ and a coarser map $P\_{i+1}$ through lateral connection and generates the new feature map $N\_{i+1}$ Each feature map $N\_{i}$ first goes through a $3 \times 3$ convolutional layer with stride $2$ to reduce the spatial size. Then each element of feature map $P\_{i+1}$ and the down-sampled map are added through lateral connection. The fused feature map is then processed by another $3 \times 3$ convolutional layer to generate $N\_{i+1}$ for following sub-networks. This is an iterative process and terminates after approaching $P\_{5}$. In these building blocks, we consistently use channel 256 of feature maps. The feature grid for each proposal is then pooled from new feature maps, i.e., {$N\_{2}$, $N\_{3}$, $N\_{4}$, $N\_{5}$}.