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

**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.

VFNet

VarifocalNet: An IoU-aware Dense Object Detector

Global Convolutional Network

Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network

**EMQAP**, or **E-Manual Question Answering Pipeline**, is an approach for answering questions pertaining to electronics devices. Built upon the pretrained [RoBERTa](https://paperswithcode.com/method/roberta), it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section.

Source	Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com