**VarifocalNet** is a method aimed at accurately ranking a huge number of candidate detections in object detection. It consists of a new loss function, named [Varifocal Loss](https://paperswithcode.com/method/varifocal-loss), for training a dense object detector to predict the IACS, and a new efficient star-shaped bounding box feature representation for estimating the IACS and refining coarse bounding boxes. Combining these two new components and a bounding box refinement branch, results in a dense object detector on the [FCOS](https://paperswithcode.com/method/fcos) architecture, what the authors call VarifocalNet or VFNet for short.

An **End-to-End Memory Network** is a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of [Memory Network](https://paperswithcode.com/method/memory-network), but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training. It can also be seen as an extension of RNNsearch to the case where multiple computational steps (hops) are performed per output symbol.

The model takes a discrete set of inputs $x\_{1}, \dots, x\_{n}$ that are to be stored in the memory, a query $q$, and outputs an answer $a$. Each of the $x\_{i}$, $q$, and $a$ contains symbols coming from a dictionary with $V$ words. The model writes all $x$ to the memory up to a fixed buffer size, and then finds a continuous representation for the $x$ and $q$. The continuous representation is then processed via multiple hops to output $a$.

End-To-End Memory Network

End-To-End Memory Networks

VFNet

VarifocalNet: An IoU-aware Dense Object Detector

A **Global Convolutional Network**, or **GCN**, is a semantic segmentation building block that utilizes a large kernel to help perform classification and localization tasks simultaneously. It can be used in a [FCN](https://paperswithcode.com/method/fcn)-like structure, where the [GCN](https://paperswithcode.com/method/gcn) is used to generate semantic score maps. Instead of directly using larger kernels or global [convolution](https://paperswithcode.com/method/convolution), the GCN module employs a combination of $1 \times k + k \times 1$ and $k \times 1 + 1 \times k$ convolutions, which enables [dense connections](https://paperswithcode.com/method/dense-connections) within a large
$k\times{k}$ region in the feature map

Source	VarifocalNet: An IoU-aware Dense Object Detector
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com