**CornerNet-Squeeze Hourglass** is a convolutional neural network and object detection backbone used in the [CornerNet-Squeeze](https://paperswithcode.com/method/cornernet-squeeze) object detector. It uses a modified [hourglass module](https://paperswithcode.com/method/hourglass-module) that makes use of a [fire module](https://paperswithcode.com/method/fire-module): containing 1x1 convolutions and depthwise convolutions.

A **HaloNet** is a self-attention based model for efficient image classification. It relies on a local self-attention architecture that efficiently maps to existing hardware with haloing. The formulation breaks translational equivariance, but the authors observe that it improves  throughput and accuracies over the centered local self-attention used in regular self-attention. The approach also utilises a strided self-attentive downsampling operation for multi-scale feature extraction.

HaloNet

Scaling Local Self-Attention for Parameter Efficient Visual Backbones

CornerNet-Squeeze Hourglass

CornerNet-Lite: Efficient Keypoint Based Object Detection

**DenseNAS** is a [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method that utilises a densely connected search space. The search space is represented as a dense super network, which is built upon designed routing blocks. In the super network, routing blocks are densely connected and we search for the best path between them to derive the final architecture. A chained cost estimation algorithm is used to approximate the model cost during the search.

Source	CornerNet-Lite: Efficient Keypoint Based Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com