**IoU-Net** is an object detection architecture that introduces localization confidence. IoU-Net learns to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective.

**DenseNet-Elastic** is a convolutional neural network that is a modification of a [DenseNet](https://paperswithcode.com/method/densenet) with elastic blocks (extra upsampling and downsampling).

DenseNet-Elastic

ELASTIC: Improving CNNs with Dynamic Scaling Policies

IoU-Net

Acquisition of Localization Confidence for Accurate Object Detection

**MoGA-A** is a convolutional neural network optimized for mobile latency and discovered via Mobile GPU-Aware (MoGA) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). The basic building block is MBConvs (inverted residual blocks) from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2). Squeeze-and-excitation layers are also experimented with.

Source	Acquisition of Localization Confidence for Accurate Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com