**CenterNet** is a one-stage object detector that detects each object as a triplet, rather than a pair, of keypoints. It utilizes two customized modules named [cascade corner pooling](https://paperswithcode.com/method/cascade-corner-pooling) and [center pooling](https://paperswithcode.com/method/center-pooling), which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. The intuition is that, if a predicted bounding box has a high IoU with the ground-truth box, then the probability that the center keypoint in its central region is predicted as the same class is high, and vice versa. Thus, during inference, after a proposal is generated as a pair of corner keypoints, we determine if the proposal is indeed an object by checking if there is a center keypoint of the same class falling within its central region.

The **Swin Transformer** is a type of [Vision Transformer](https://paperswithcode.com/method/vision-transformer). It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.

Swin Transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

CenterNet

CenterNet: Keypoint Triplets for Object Detection

Our model known as CubeRE first encodes each input sentence using a language model encoder to obtain the contextualized sequence representation. We then capture the interaction between each possible head and tail entity as a pair representation for predicting the entity-relation label scores. To reduce the computational cost, each sentence is pruned to retain only words that have higher entity scores. Finally, we capture the interaction between each possible relation triplet and qualifier to predict the qualifier label scores and decode the outputs.

Source	CenterNet: Keypoint Triplets for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com