An **Recursive Feature Pyramid (RFP)** builds on top of the Feature Pyramid Networks ([FPN](https://paperswithcode.com/method/fpn)) by incorporating extra feedback connections from the FPN layers into the bottom-up backbone layers. Unrolling the recursive structure to a sequential implementation, we obtain a backbone for object detector that looks at the images twice or more. Similar to the cascaded detector heads in [Cascade R-CNN](https://paperswithcode.com/method/cascade-r-cnn) trained with more selective examples, an RFP recursively enhances FPN to generate increasingly powerful representations. Resembling Deeply-Supervised Nets, the feedback connections bring the features that directly receive gradients from the detector heads back to the low levels of the bottom-up backbone to speed up training and boost performance.

**Mode Normalization** extends normalization to more than a single mean and variance, allowing for detection of modes of data on-the-fly, jointly normalizing samples that share common features. It first assigns samples in a mini-batch to different modes via a gating network, and then normalizes each sample with estimators for its corresponding mode.

Mode Normalization

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

VERtex Similarity Embeddings (VERSE) is a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network.

Source: [Tsitsulin et al.](https://arxiv.org/pdf/1803.04742v1.pdf)

Image source: [Tsitsulin et al.](https://arxiv.org/pdf/1803.04742v1.pdf)

Source	DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com